The Artificial Intelligence Infrastructure Workshop
The Artificial Intelligence Infrastructure Workshop

Author: Chinmay Arankalle

Publisher: Packt Publishing Ltd

Published: 2020-08-17

Total Pages: 731

ISBN-13: 1800206992

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Explore how a data storage system works – from data ingestion to representation Key FeaturesUnderstand how artificial intelligence, machine learning, and deep learning are different from one anotherDiscover the data storage requirements of different AI apps using case studiesExplore popular data solutions such as Hadoop Distributed File System (HDFS) and Amazon Simple Storage Service (S3)Book Description Social networking sites see an average of 350 million uploads daily - a quantity impossible for humans to scan and analyze. Only AI can do this job at the required speed, and to leverage an AI application at its full potential, you need an efficient and scalable data storage pipeline. The Artificial Intelligence Infrastructure Workshop will teach you how to build and manage one. The Artificial Intelligence Infrastructure Workshop begins taking you through some real-world applications of AI. You'll explore the layers of a data lake and get to grips with security, scalability, and maintainability. With the help of hands-on exercises, you'll learn how to define the requirements for AI applications in your organization. This AI book will show you how to select a database for your system and run common queries on databases such as MySQL, MongoDB, and Cassandra. You'll also design your own AI trading system to get a feel of the pipeline-based architecture. As you learn to implement a deep Q-learning algorithm to play the CartPole game, you'll gain hands-on experience with PyTorch. Finally, you'll explore ways to run machine learning models in production as part of an AI application. By the end of the book, you'll have learned how to build and deploy your own AI software at scale, using various tools, API frameworks, and serialization methods. What you will learnGet to grips with the fundamentals of artificial intelligenceUnderstand the importance of data storage and architecture in AI applicationsBuild data storage and workflow management systems with open source toolsContainerize your AI applications with tools such as DockerDiscover commonly used data storage solutions and best practices for AI on Amazon Web Services (AWS)Use the AWS CLI and AWS SDK to perform common data tasksWho this book is for If you are looking to develop the data storage skills needed for machine learning and AI and want to learn AI best practices in data engineering, this workshop is for you. Experienced programmers can use this book to advance their career in AI. Familiarity with programming, along with knowledge of exploratory data analysis and reading and writing files using Python will help you to understand the key concepts covered.

Artificial Intelligence, Co-Creation and Creativity
Artificial Intelligence, Co-Creation and Creativity

Author: Francisco Tigre Moura

Publisher: Taylor & Francis

Published: 2024-08-01

Total Pages: 255

ISBN-13: 1040112544

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Artificial intelligence (AI) has deeply impacted our understanding of creativity and the human ability to generate creative outputs. New applications for creative tasks are rapidly evolving, and new tools are constantly being developed with much greater optimal capabilities. Importantly, the success of implementing such tools for creative tasks is still heavily dependent on human supervision and input. Therefore, it is vital to understand and critically reflect on the nature of co-creative processes between humans and AI. This book addresses such issues and provides insights into how humans can augment their capabilities for generating creative and innovative outputs by successfully co-creating with AI. The book is intentionally divided into three main parts to allow for a comprehensive and holistic perspective on human and AI co-creation for creative tasks. The sections are divided as follows: Part 1: “Principles of AI and Creativity”, Part 2: “Critical Issues on Artificial Co-Creation”, and Part 3: “Industry-Specific Discussions”. Consequently, the book provides a holistic insight on the topic, covering various issues and perspectives and enabling an accessible read to a broad audience. For example, chapters cover examples across different industry sectors, including music, arts, science, and management. Furthermore, the book covers critical questions involving copyrights, ethical concerns, relationship with algorithms, and context-based issues. Only by critically reflecting on the intrinsic issues of AI and learning how to work with it effectively for creative purposes will we be able to benefit from its full potential to augment human creative abilities in an appropriate manner. This novel, edited collection is an essential read for scholars working on the intersection of AI, creativity, arts, and management.

Machine Learning for Streaming Data with Python
Machine Learning for Streaming Data with Python

Author: Joos Korstanje

Publisher: Packt Publishing Ltd

Published: 2022-07-15

Total Pages: 258

ISBN-13: 1803242639

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Apply machine learning to streaming data with the help of practical examples, and deal with challenges that surround streaming Key Features • Work on streaming use cases that are not taught in most data science courses • Gain experience with state-of-the-art tools for streaming data • Mitigate various challenges while handling streaming data Book Description Streaming data is the new top technology to watch out for in the field of data science and machine learning. As business needs become more demanding, many use cases require real-time analysis as well as real-time machine learning. This book will help you to get up to speed with data analytics for streaming data and focus strongly on adapting machine learning and other analytics to the case of streaming data. You will first learn about the architecture for streaming and real-time machine learning. Next, you will look at the state-of-the-art frameworks for streaming data like River. Later chapters will focus on various industrial use cases for streaming data like Online Anomaly Detection and others. As you progress, you will discover various challenges and learn how to mitigate them. In addition to this, you will learn best practices that will help you use streaming data to generate real-time insights. By the end of this book, you will have gained the confidence you need to stream data in your machine learning models. What you will learn • Understand the challenges and advantages of working with streaming data • Develop real-time insights from streaming data • Understand the implementation of streaming data with various use cases to boost your knowledge • Develop a PCA alternative that can work on real-time data • Explore best practices for handling streaming data that you absolutely need to remember • Develop an API for real-time machine learning inference Who this book is for This book is for data scientists and machine learning engineers who have a background in machine learning, are practice and technology-oriented, and want to learn how to apply machine learning to streaming data through practical examples with modern technologies. Although an understanding of basic Python and machine learning concepts is a must, no prior knowledge of streaming is required.

Robotics and AI for Cybersecurity and Critical Infrastructure in Smart Cities
Robotics and AI for Cybersecurity and Critical Infrastructure in Smart Cities

Author: Nadia Nedjah

Publisher: Springer Nature

Published: 2022-03-28

Total Pages: 267

ISBN-13: 3030967379

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This book bridges principles and real-world applications, while also providing thorough theory and technology for the development of artificial intelligence and robots. A lack of cross-pollination between AI and robotics research has led to a lack of progress in both fields. Now that both technologies have made significant strides, there is increased interest in combining the two domains in order to create a new integrated AI and robotics trend. In order to achieve wiser urbanization and more sustainable development, AI in smart cities will play a significant part in equipping the cities with advanced features that will allow residents to safely move about, stroll, shop, and enjoy a more comfortable way of life. If you are a student, researcher, engineer, or professional working in this field, or if you are just curious in the newest advancements in robotics and artificial intelligence for cybersecurity, this book is for you!

Applied Public Key Infrastructure
Applied Public Key Infrastructure

Author: Jianying Zhou

Publisher: IOS Press

Published: 2005

Total Pages: 276

ISBN-13: 1586035509

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Includes topics such as: Public Key Infrastructure (PKI) Operation and Case Study, Non-repudiation, Authorization and Access Control, Authentication and Time-Stamping, Certificate Validation and Revocation, and Cryptographic Applications.

Materials Discovery and Design
Materials Discovery and Design

Author: Turab Lookman

Publisher: Springer

Published: 2018-09-22

Total Pages: 266

ISBN-13: 3319994654

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This book addresses the current status, challenges and future directions of data-driven materials discovery and design. It presents the analysis and learning from data as a key theme in many science and cyber related applications. The challenging open questions as well as future directions in the application of data science to materials problems are sketched. Computational and experimental facilities today generate vast amounts of data at an unprecedented rate. The book gives guidance to discover new knowledge that enables materials innovation to address grand challenges in energy, environment and security, the clearer link needed between the data from these facilities and the theory and underlying science. The role of inference and optimization methods in distilling the data and constraining predictions using insights and results from theory is key to achieving the desired goals of real time analysis and feedback. Thus, the importance of this book lies in emphasizing that the full value of knowledge driven discovery using data can only be realized by integrating statistical and information sciences with materials science, which is increasingly dependent on high throughput and large scale computational and experimental data gathering efforts. This is especially the case as we enter a new era of big data in materials science with the planning of future experimental facilities such as the Linac Coherent Light Source at Stanford (LCLS-II), the European X-ray Free Electron Laser (EXFEL) and MaRIE (Matter Radiation in Extremes), the signature concept facility from Los Alamos National Laboratory. These facilities are expected to generate hundreds of terabytes to several petabytes of in situ spatially and temporally resolved data per sample. The questions that then arise include how we can learn from the data to accelerate the processing and analysis of reconstructed microstructure, rapidly map spatially resolved properties from high throughput data, devise diagnostics for pattern detection, and guide experiments towards desired targeted properties. The authors are an interdisciplinary group of leading experts who bring the excitement of the nascent and rapidly emerging field of materials informatics to the reader.

Leveraging Artificial Intelligence in Engineering, Management, and Safety of Infrastructure
Leveraging Artificial Intelligence in Engineering, Management, and Safety of Infrastructure

Author: M.Z. Naser

Publisher: CRC Press

Published: 2022-11-17

Total Pages: 459

ISBN-13: 1000788997

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The design, construction, and upkeep of infrastructure is comprised of a multitude of dimensions spanning a highly complex paradigm of interconnected opportunities and challenges. While traditional methods fall short of adequately accounting for such complexity, artificial intelligence (AI) presents novel and out-of-the-box solutions that effectively tackle the growing demands of our infrastructure. The convergence between AI and civil engineering is an emerging frontier with tremendous potential. The book is likely to provide a boost to the state of infrastructure engineering by fostering a new look at civil engineering that capitalizes on AI as its main driver. It highlights the ongoing push to adopt and leverage AI to realize contemporary, intelligent, safe, and resilient infrastructure. The book comprises interdisciplinary and novel works from across the globe. It presents findings from innovative efforts supplemented with physical tests, numerical simulations, and case studies – all of which can be used as benchmarks to carry out future experiments and/or facilitate the development of future AI models in structural engineering, traffic engineering, construction engineering, and construction materials. The book will serve as a guide for a wide range of audiences, including senior undergraduate and graduate students, professionals, and government officials of civil, traffic, and computer engineering backgrounds, as well as for those engaged in urban planning and human sciences.

Infrastructure for Agents, Multi-Agent Systems, and Scalable Multi-Agent Systems
Infrastructure for Agents, Multi-Agent Systems, and Scalable Multi-Agent Systems

Author: Tom Wagner

Publisher: Springer

Published: 2003-08-06

Total Pages: 320

ISBN-13: 3540477721

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Building research grade multi-agent systems usually involves a broad variety of software infrastructure ingredients like planning, scheduling, coordination, communication, transport, simulation, and module integration technologies and as such constitutes a great challenge to the individual researcher active in the area. The book presents a collection of papers on approaches that will help make deployed and large scale multi-agent systems a reality. The first part focuses on available infrastructure and requirements for constructing research-grade agents and multi-agent systems. The second part deals with support in infrastructure and software development methods for multi-agent systems that can directly support coordination and management of large multi-agent communities; performance analysis and scalability techniques are needed to promote deployment of multi-agent systems to professionals in software engineering and information technology.