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 Название: Artificial General Intelligence
Автор: Julian Togelius
Издательство: The MIT Press
Год: 2024
Страниц: 238
Язык: английский
Формат: pdf (true), epub (true)
Размер: 10.1 MB
How to make AI capable of general intelligence, and what such technology would mean for society. Artificial Intelligence (AI) surrounds us. More and more of the systems and services you interact with every day are based on AI technology. Although some very recent AI systems are generalists to a degree, most AI is narrowly specific; that is, it can only do a single thing, in a single context. For example, your spellchecker can’t do mathematics, and the world's best chess-playing program can’t play Tetris. Human intelligence is different. We can solve a variety of tasks, including those we have not seen before. In Artificial General Intelligence, Julian Togelius explores technical approaches to developing more general Artificial Intelligence and asks what general AI would mean for human civilization. Togelius starts by giving examples of narrow AI that have superhuman performance in some way. Interestingly, there have been AI systems that are superhuman in some sense for more than half a century. He then discusses what it would mean to have general intelligence, by looking at definitions from psychology, ethology, and Computer Science. Next, he explores the two main families of technical approaches to developing more general Artificial Intelligence: foundation models through self-supervised learning, and open-ended learning in virtual environments. The final chapters of the book investigate potential Artificial General Intelligence beyond the strictly technical aspects. The questions discussed here investigate whether such general AI would be conscious, whether it would pose a risk to humanity, and how it might alter society. | |
Разместил: Ingvar16 10-10-2024, 15:27 | Комментарии: 0 | Подробнее
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 Название: Platform Engineering: A Guide for Technical, Product, and People Leaders
Автор: Camille Fournier, Ian Nowland
Издательство: O’Reilly Media, Inc.
Год: 2024
Страниц: 361
Язык: английский
Формат: pdf, epub
Размер: 10.1 MB
Until recently, infrastructure was the backbone of organizations operating software they developed in-house. But now that cloud vendors run the computers, companies can finally bring the benefits of agile custom-centricity to their own developers. Adding product management to infrastructure organizations is now all the rage. But how's that possible when infrastructure is still the operational layer of the company? This practical book guides engineers, managers, product managers, and leaders through the shifts that modern platform-led organizations require. You'll learn what platform engineering is—and isn't—and what benefits and value it brings to developers and teams. You'll understand what it means to approach a platform as a product and learn some of the most common technical and managerial barriers to success. Finally, this book is really for anyone interested in learning how to make platform engineering work beyond the technical implementation details. Whether you are at a startup wondering when to start, a big company thinking about moving from infrastructure engineering to platform engineering, or anywhere in between, this book is for you. | |
Разместил: Ingvar16 10-10-2024, 04:47 | Комментарии: 0 | Подробнее
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 Название: Genomics at the Nexus of AI, Computer Vision, and Machine Learning
Автор: Shilpa Choudhary, Sandeep Kumar, Swathi Gowroju, Monali Gulhane, R. Sri Lakshmi
Издательство: Wiley-Scrivener
Год: 2025
Страниц: 540
Язык: английский
Формат: pdf (true)
Размер: 36.8 MB
Genomics at the Nexus of AI, Computer Vision, and Machine Learning explores the in-depth process of how AI and Machine Learning algorithms extract genomic data. The main goal is to help readers understand the dynamic intersection between genomics and cutting-edge technologies. This book aims to provide a roadmap for navigating genomics with developments in Artificial Intelligence (AI) to open up new research ideas to detect and analyze genetic patterns using Computer Vision methods. This book encompasses a wide range of topics, starting with an introduction to genomics data and its unique characteristics. Each chapter unfolds a unique facet, delving into the collaborative potential and challenges that arise from advanced technologies. It explores image analysis techniques specifically tailored for genomic data. With this resourceful data, research enables the detection and analysis of genetic patterns using Computer Vision methods. Furthermore, the dedicated research from contributors offers insights and knowledge to genomic research that seeks to explore the mysteries of life through the lens of interdisciplinary collaboration. | |
Разместил: Ingvar16 9-10-2024, 19:07 | Комментарии: 0 | Подробнее
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 Название: Introduction to Microstation VBA: Microstation Connect Edition
Автор: Saeed Murray
Издательство: Independently published
Год: October 6, 2024
Страниц: 266
Язык: английский
Формат: pdf, epub, mobi
Размер: 10.1 MB
This book provides a comprehensive guide to automating tasks in MicroStation using Visual Basic for Applications (VBA). Whether you are new to programming or an experienced MicroStation user looking to enhance your workflows, this book offers a step-by-step approach to mastering VBA in the MicroStation environment. Starting with the basics of the VBA IDE, variables, and code structure, the book guides you through fundamental programming concepts before diving into MicroStation-specific tasks. You'll learn how to create and manipulate elements, automate drawing processes, and interact with external applications like Excel for data import/export. Through practical projects, and case studies, this book ensures that you gain hands-on experience with VBA in MicroStation. Whether you’re automating simple workflows or tackling complex design problems, this guide will equip you with the tools you need to become proficient in VBA programming for MicroStation. By the end of the book, you will have developed a solid foundation in VBA, with the ability to create custom applications, automate design processes, and significantly improve your efficiency within MicroStation. This book is a valuable resource for MicroStation users, CAD professionals, and engineers looking to harness the full power of VBA for automating tasks and enhancing their design workflows. | |
Разместил: Ingvar16 9-10-2024, 17:21 | Комментарии: 0 | Подробнее
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 Название: Effect Oriented Programming : A Paradigm for Creating Reliable, Adaptable, Testable Systems - Using Scala and ZIO
Автор: Bill Frasure, Bruce Eckel, James Ward
Издательство: Leanpub
Год: 2024-09-17
Страниц: 147
Язык: английский
Формат: pdf (true), epub, mobi
Размер: 10.1 MB
Have you wondered what makes functional programming such a big deal, but haven't been able to get through any of the explanations? We wrote this book for you. Effects are the unpredictable elements in your programs. Concerns like network communication or user interaction might seem easy, but they are devilishly difficult to get right. The pristine world of algorithms devolves into the gory reality of failures and inconsistency. Traditionally, we've coped with Effects incompletely and often unwittingly. Programs have been difficult to build, adapt, and maintain. Discover a groundbreaking approach to software development using Effect Systems to control the unpredictable elements in your systems. We focus on practical techniques you can apply immediately, making complex concepts accessible to all developers. You'll learn resilient system development in a straightforward, pragmatic way, using simplified code examples and clear explanatory prose. Effect Oriented Programming is a new paradigm for programming with Effect Systems. Since Effect Systems are a new and emerging paradigm, you have limited choices. Many programming languages do not have an Effect System. Some languages have built-in support for managing Effects, while others have support through libraries. New languages that incorporate Effect Systems include OCaml, Unison, and Roc. In this book, we focus on the concepts of Effect Systems, rather than language and library specifics. We use Scala 3, which has several Effect System libraries including ZIO, Cats Effects, and Kyo. These libraries (and others) contributed to our understanding of Effect Systems. This is not a book about ZIO. You do not need experience or understanding of ZIO to understand the code in this book. For this book, ZIO is only a means to understand Effect Systems. If you use a different language, the concepts of Effect Systems may only be useful when your language or a library supports them. | |
Разместил: Ingvar16 9-10-2024, 16:34 | Комментарии: 0 | Подробнее
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 Название: Deep Reinforcement Learning and Its Industrial Use Cases: AI for Real-World Applications
Автор: Shubham Mahajan, Pethuru Raj, Amit Kant Pandit
Издательство: Wiley-Scrivener
Год: 2025
Страниц: 403
Язык: английский
Формат: pdf (true)
Размер: 46.6 MB
“Deep Reinforcement Learning and its Industrial Use Cases: Harnessing AI for Real-World Applications” is an essential guide that supplies complex theories, practical insights, and diverse case studies behind deep reinforcement learning. This book offers a comprehensive look into how DLR is revolutionizing fields by implementing advanced algorithms in a variety of industries to solve real-world problems. Beyond the realm of successes of DLR, it critically examines challenges, pitfalls, and ethical considerations. This research and knowledge explore insights to meet the needs of curious enthusiasts eager to understand the cutting-edge technology shaping our future. Throughout the pages of this book, we seek to discover the inner workings of DLR and its real-world applications. We start by laying down the foundational principles of reinforcement learning and building up to advanced DLR algorithms and techniques. Along the way, we delve into diverse case studies, examining how leading organizations harness the power of DLR to drive innovation and gain a competitive edge. From financial trading to autonomous manufacturing systems, each case study offers valuable insights into the practical considerations and challenges involved in deploying DLR solutions. Deep reinforcement learning is a Machine Learning subfield that deals with teaching agents to make successive decisions in a given environment in order to maximize a cumulative reward. DRL algorithms approximation rules or function values with neural networks, allowing agents to learn sophisticated behaviors directly from raw sensory input. | |
Разместил: Ingvar16 9-10-2024, 06:08 | Комментарии: 0 | Подробнее
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 Название: Artificial Intelligence, Machine Learning and User Interface Design
Автор: Abhijit Banubakode, Sunita Dhotre, Chhaya S. Gosavi, G.S. Mate
Издательство: Bentham Science Publishers
Год: 2024
Страниц: 460
Язык: английский
Формат: pdf, epub
Размер: 10.3 MB
Artificial Intelligence, Machine Learning and User Interface Design is a forward-thinking compilation of reviews that explores the intersection of Artificial Intelligence (AI), Machine Learning (ML) and User Interface (UI) design. The book showcases recent advancements, emerging trends and the transformative impact of these technologies on digital experiences and technologies. The editors have compiled 14 multidisciplinary topics contributed by over 40 experts, covering foundational concepts of AI and ML, and progressing through intricate discussions on recent algorithms and models. Case studies and practical applications illuminate theoretical concepts, providing readers with actionable insights. From neural network architectures to intuitive interface prototypes, the book covers the entire spectrum, ensuring a holistic understanding of the interplay between these domains. Use cases of AI and ML highlighted in the book include categorization and management of waste, taste perception of tea, bird species identification, content-based image retrieval, natural language processing (NLP), code clone detection, knowledge representation, tourism recommendation systems and solid waste management. Advances in Artificial Intelligence, Machine Learning and User Interface Design aims to inform a diverse readership, including Computer Science students, AI and ML software engineers, UI/UX designers, researchers, and tech enthusiasts. | |
Разместил: Ingvar16 9-10-2024, 04:04 | Комментарии: 0 | Подробнее
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 Название: Introduction to Classifier Performance Analysis with R
Автор: Sutaip L.C. Saw
Издательство: CRC Press
Серия: Data Science Series
Год: 2025
Страниц: 222
Язык: английский
Формат: pdf (true), epub
Размер: 10.1 MB
Classification problems are common in business, medicine, science, engineering and other sectors of the economy. Data scientists and Machine Learning professionals solve these problems through the use of classifiers. Choosing one of these data driven classification algorithms for a given problem is a challenging task. An important aspect involved in this task is classifier performance analysis (CPA). Introduction to Classifier Performance Analysis with R provides an introductory account of commonly used CPA techniques for binary and multiclass problems, and use of the R software system to accomplish the analysis. Coverage draws on the extensive literature available on the subject, including descriptive and inferential approaches to CPA. Exercises are included at the end of each chapter to reinforce learning. This book is for those who want a reasonably complete (at least at an introductory level) and up-to-date coverage on the analysis of classification algorithms through the use of performance measures and curves. It attempts to synthesize useful material from the vast published literature on the subject. Another motivation for the book is to show how R can be used to perform the required analysis. As computational software, R has already demonstrated its excellence to a large international community of users. Its appeal is further enhanced by recently developed packages and meta-packages for Data Science, Machine Learning, and classification performance analysis in particular. This is a useful resource for upper level undergraduate and masters level students in Data Science, Machine Learning and related disciplines. Practitioners interested in learning how to use R to evaluate classifier performance can also potentially benefit from the book. The material and references in the book can also serve the needs of researchers in CPA. | |
Разместил: Ingvar16 8-10-2024, 18:44 | Комментарии: 0 | Подробнее
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 Название: Solve Any Data Analysis Problem: Eight projects that show you how (MEAP v8)
Автор: David Asboth
Издательство: Manning Publications
Год: 2024
Страниц: 562
Язык: английский
Формат: pdf (true)
Размер: 91.8 MB
Complete eight Data Science projects that lock in important real world skills–along with a practical process you can use to learn any new technique quickly and efficiently. Solve Any Data Analysis Problem guides you through eight common scenarios you'll encounter as a data scientist or analyst. As you explore each project, you’ll also master a proven process for quickly learning new skills developed by author and Half Stack Data Science podcast host David Asboth. In Solve Any Data Analysis Problem you’ll learn how to shift the way you think about data from the structured clean problems you get in a classroom, book, or bootcamp to the messy open-ended challenges of the workplace. As you work through eight problems you’ll see over and over on the job, you’ll discover a solutions-driven methodology that’s focused on getting results. You’ll learn how to determine a minimum viable answer for your stakeholders, identify and obtain the data you need to deliver, and reliably present and iterate on your findings. Which tool you are comfortable doing the above in does not matter. I will provide example solutions to the projects in the book using Python, but the focus will be on problem-solving, not the specifics of the Python programming language. Appendix A gives you a quick overview of the skills and tools in the basic Data Science toolkit. If you need to brush up on anything, we’ve linked to some useful resources you can use to get up to speed. As for most solutions I provide, the code itself will be written in Python, primarily using the Pandas library. While code snippets will be used to explain the example solution, I will focus discussions on the conceptual solution and less on the specifics of the code. The solution will be in three parts: setting up the problem statement and the data, creating the first iteration of a solution, and a third part to review the work and decide on further steps. | |
Разместил: Ingvar16 8-10-2024, 16:20 | Комментарии: 0 | Подробнее
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 Название: Starting Data Analytics with Generative AI and Python
Автор: Artur Guja, Marlena Siwiak, Marian Siwiak
Издательство: Manning Publications
Год: 2025
Страниц: 362
Язык: английский
Формат: pdf (true)
Размер: 15.5 MB
Accelerate your mastery of data analytics with the power of ChatGPT. Whether you’re brand new to data analysis or an experienced pro looking to do more work, faster, Starting Data Analytics with Generative AI and Python is here to help simplify and speed up your data analysis! Written by a pair of world-class data scientists and an experienced risk manager, the book concentrates on the practical analytics tasks you'll do every day. In Starting Data Analytics with Generative AI and Python you’ll learn how to improve your coding efficiency, generate new analytical approaches, and fine-tune data pipelines—all assisted by AI tools like ChatGPT. For each step in the data process, you’ll discover how ChatGPT can implement data techniques from simple plain-English prompts. Plus, you’ll develop a vital intuition about the risks and errors that still come with these tools. If you have basic knowledge of data analysis, this book will show you how to use ChatGPT to accelerate your essential data analytics work. This speed-up can be amazing: the authors report needing one third or even one quarter the time they needed before. You’ll find reliable and practical advice that works on the job. Improve problem exploration, generate new analytical approaches, and fine-tune your data pipelines—all while developing an intuition about the risks and errors that still come with AI tools. Assuming only that you know the foundations, this friendly book guides you through the entire analysis process—from gathering and preparing raw data, data cleaning, generating code-based solutions, selecting statistical tools, and finally creating effective data presentations. With clearly-explained prompts to extract, interpret, and present data, it will raise your skills to a whole different level. | |
Разместил: Ingvar16 8-10-2024, 15:23 | Комментарии: 0 | Подробнее
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