Deployable Multimodal Machine Intelligence


Book Description

This book highlights the principles, design and characterization of mechanically compliant soft and foldable robots. Traditional rigid robots with bulky footprints and complicated components prolong the design iteration and optimization for keyhole and minimally invasive transluminal applications. Therefore, there is an interest in developing soft and foldable robots with remote actuation, multimodal sensing and machine intelligence. This book discusses the use of foldable and cuttable structures to design biomimetic deployable soft robots, that can exhibit a fair number of motions with consistency and repeatability. It presents the overall design principles, methodology, instrumentation, metamorphic sensing, multi-modal perception, and machine intelligence for creating untethered foldable active structures. These robotic structures can generate a variety of motions such as wave induction, compression, inchworm, peristalsis, flipping, tumbling, walking, swimming, flexion/extension etc. Remote actuation can control motions along regular and irregular surfaces from proximal sides. For self-deployable medical robots, motion diversity and shape reconfiguration are crucial factors. Deployable robots, with the use of malleable and resilient smart actuators, hold this crucial advantage over their conventional rigid robot counterparts. Such flexible structures capable of being compressed and expanded with intelligence perceptions hold enormous potential in biomedical applications.




Machine Learning for Multimodal Interaction


Book Description

This book constitutes the thoroughly refereed post-proceedings of the Second International Workshop on Machine Learning for Multimodal Interaction held in July 2005. The 38 revised full papers presented together with two invited papers were carefully selected during two rounds of reviewing and revision. The papers are organized in topical sections on multimodal processing, HCI and applications, discourse and dialogue, emotion, visual processing, speech and audio processing, and NIST meeting recognition evaluation.




Generative AI on AWS


Book Description

Companies today are moving rapidly to integrate generative AI into their products and services. But there's a great deal of hype (and misunderstanding) about the impact and promise of this technology. With this book, Chris Fregly, Antje Barth, and Shelbee Eigenbrode from AWS help CTOs, ML practitioners, application developers, business analysts, data engineers, and data scientists find practical ways to use this exciting new technology. You'll learn the generative AI project life cycle including use case definition, model selection, model fine-tuning, retrieval-augmented generation, reinforcement learning from human feedback, and model quantization, optimization, and deployment. And you'll explore different types of models including large language models (LLMs) and multimodal models such as Stable Diffusion for generating images and Flamingo/IDEFICS for answering questions about images. Apply generative AI to your business use cases Determine which generative AI models are best suited to your task Perform prompt engineering and in-context learning Fine-tune generative AI models on your datasets with low-rank adaptation (LoRA) Align generative AI models to human values with reinforcement learning from human feedback (RLHF) Augment your model with retrieval-augmented generation (RAG) Explore libraries such as LangChain and ReAct to develop agents and actions Build generative AI applications with Amazon Bedrock




Multimodal Intelligent Sensing in Modern Applications


Book Description

Discover the design, implementation, and analytical techniques for multi-modal intelligent sensing in this cutting-edge text The Internet of Things (IoT) is becoming ever more comprehensively integrated into everyday life. The intelligent systems that power smart technologies rely on increasingly sophisticated sensors in order to monitor inputs and respond dynamically. Multi-modal sensing offers enormous benefits for these technologies, but also comes with greater challenges; it has never been more essential to offer energy-efficient, reliable, interference-free sensing systems for use with the modern Internet of Things. Multimodal Intelligent Sensing in Modern Applications provides an introduction to systems which incorporate multiple sensors to produce situational awareness and process inputs. It is divided into three parts—physical design aspects, data acquisition and analysis techniques, and security and energy challenges—which together cover all the major topics in multi-modal sensing. The result is an indispensable volume for engineers and other professionals looking to design the smart devices of the future. Multimodal Intelligent Sensing in Modern Applications readers will also find: A field of multidisciplinary contributors in fields like wireless communications, signal processing, and sensor design Coverage of both software and hardware solutions to sensing challenges Detailed treatment of advanced topics like efficient deployment, data fusion, machine learning, and more Multimodal Intelligent Sensing in Modern Applications is ideal for experienced engineers and designers who need to apply their skills to Internet of Things and 5G/6G networks. It can also act an introductory text for graduate researchers into understanding the background, design, and implementation of various sensor types and data analytics tools.




Artificial Intelligence and Machine Learning for Smart Community


Book Description

Intelligent systems are technologically advanced machines that perceive and respond to the world around them. Artificial Intelligence and Machine Learning for Smart Community: Concepts and Applications presents the evolution, challenges, and limitations of the application of machine learning and artificial intelligence to intelligent systems and smart communities. Covers the core and fundamental aspects of artificial intelligence, machine learning, and computational algorithms in smart intelligent systems Discusses the integration of artificial intelligence with machine learning using mathematical modeling Elaborates concepts like supervised and unsupervised learning, and machine learning algorithms, such as linear regression, logistic regression, random forest, and performance evaluation matrices Introduces modern algorithms such as convolutional neural networks and support vector machines Presents case studies on smart healthcare, smart traffic management, smart buildings, autonomous vehicles, smart education, modern community, and smart machines Artificial Intelligence and Machine Learning for Smart Community: Concepts and Applications is primarily written for graduate students and academic researchers working in the fields of computer science and engineering, electrical engineering, and information technology. Seasonal Blurb: This reference text presents the most recent and advanced research on the application of artificial intelligence and machine learning on intelligent systems. It will discuss important topics such as business intelligence, reinforcement learning, supervised learning, and unsupervised learning in a comprehensive manner.




Interpretability of Machine Intelligence in Medical Image Computing and Multimodal Learning for Clinical Decision Support


Book Description

This book constitutes the refereed joint proceedings of the Second International Workshop on Interpretability of Machine Intelligence in Medical Image Computing, iMIMIC 2019, and the 9th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2019, held in conjunction with the 22nd International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2019, in Shenzhen, China, in October 2019. The 7 full papers presented at iMIMIC 2019 and the 3 full papers presented at ML-CDS 2019 were carefully reviewed and selected from 10 submissions to iMIMIC and numerous submissions to ML-CDS. The iMIMIC papers focus on introducing the challenges and opportunities related to the topic of interpretability of machine learning systems in the context of medical imaging and computer assisted intervention. The ML-CDS papers discuss machine learning on multimodal data sets for clinical decision support and treatment planning.




Advances in Scalable and Intelligent Geospatial Analytics


Book Description

Geospatial data acquisition and analysis techniques have experienced tremendous growth in the last few years, providing an opportunity to solve previously unsolved environmental- and natural resource-related problems. However, a variety of challenges are encountered in processing the highly voluminous geospatial data in a scalable and efficient manner. Technological advancements in high-performance computing, computer vision, and big data analytics are enabling the processing of big geospatial data in an efficient and timely manner. Many geospatial communities have already adopted these techniques in multidisciplinary geospatial applications around the world. This book is a single source that offers a comprehensive overview of the state of the art and future developments in this domain. FEATURES Demonstrates the recent advances in geospatial analytics tools, technologies, and algorithms Provides insight and direction to the geospatial community regarding the future trends in scalable and intelligent geospatial analytics Exhibits recent geospatial applications and demonstrates innovative ways to use big geospatial data to address various domain-specific, real-world problems Recognizes the analytical and computational challenges posed and opportunities provided by the increased volume, velocity, and veracity of geospatial data This book is beneficial to graduate and postgraduate students, academicians, research scholars, working professionals, industry experts, and government research agencies working in the geospatial domain, where GIS and remote sensing are used for a variety of purposes. Readers will gain insights into the emerging trends on scalable geospatial data analytics.







Deep Learning Model Optimization, Deployment and Improvement Techniques for Edge-native Applications


Book Description

The edge AI implementation technologies are fast maturing and stabilizing. Edge AI digitally transforms retail, manufacturing, healthcare, financial services, transportation, telecommunication, and energy. The transformative potential of Edge AI, a pivotal force in driving the evolution from Industry 4.0’s smart manufacturing and automation to Industry 5.0’s human-centric, sustainable innovation. The exploration of the cutting-edge technologies, tools, and applications that enable real-time data processing and intelligent decision-making at the network’s edge, addressing the increasing demand for efficiency, resilience, and personalization in industrial systems. Our book aims to provide readers with a comprehensive understanding of how Edge AI integrates with existing infrastructures, enhances operational capabilities, and fosters a symbiotic relationship between human expertise and machine intelligence. Through detailed case studies, technical insights, and practical guidelines, this book serves as an essential resource for professionals, researchers, and enthusiasts poised to harness the full potential of Edge AI in the rapidly advancing industrial landscape.




Proceedings of the 7th Annual GIFT Users Symposium


Book Description

These proceedings are intended to document the evolutions of GIFT as a tool for the authoring of intelligent tutoring systems (ITSs) and the evaluation of adaptive instructional tools and methods. Papers in this volume were selected with the following goals in mind: (1) The candidate papers describe tools and methods that raise the level of knowledge and/or capability in the ITS research and development community (2) The candidate papers describe research, features, or practical applications of GIFT (3) The candidate papers expand ITSs into previously untapped domains (4) The candidate papers build/expand models of automated instruction for individuals and/or teams