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| chat gpt architecture diagram: The future of education: Integrating AI in the classroom Balasubramanian Thiagarajan, 2024-10-07 In recent years, **Artificial Intelligence (AI)** has rapidly transformed many industries, and education is no exception. As the world embraces the digital age, AI is poised to become an integral part of the educational landscape, reshaping how we teach, learn, and manage educational systems. This book, *The Future of Education: Integrating AI in the Classroom*, explores the profound impact AI is having on education and offers a glimpse into the future of learning in an AI-driven world. The journey to transform education through AI has only just begun, but the potential is immense. AI offers unparalleled opportunities to personalize learning, automate administrative tasks, and create smarter, more engaging learning environments. Through AI, educators can identify the unique needs of each student, providing customized learning paths that adjust in real-time based on a student’s progress. Meanwhile, AI-powered tools allow teachers to focus more on inspiring creativity, critical thinking, and problem-solving, rather than getting bogged down in time-consuming tasks like grading. This book delves into these opportunities and challenges, providing educators, administrators, and policymakers with insights into the current and future applications of AI in education. It highlights how AI is helping to create more equitable learning environments, enabling even the most underserved students to access high-quality education. At the same time, the book discusses the ethical considerations of AI—ensuring that the use of AI technologies is inclusive, unbiased, and respects students’ privacy. Through practical strategies and real-world applications, this book offers a roadmap for integrating AI into the classroom effectively. It is designed to empower educators with the knowledge and tools to harness AI in ways that enhance teaching and learning, foster collaboration, and drive educational innovation. As we embark on this exciting journey, it is essential to recognize that AI will not replace teachers but instead serve as a powerful tool to augment their capabilities. By doing so, we can ensure that the future of education is not only more efficient but also more personalized, engaging, and impactful for every learner. |
| chat gpt architecture diagram: MultiMedia Modeling Stevan Rudinac, |
| chat gpt architecture diagram: Applying UML and Patterns: An Introduction to Object Oriented Analysis and Design and Interative Development: 3rd Edition Craig Larman, 2012 |
| chat gpt architecture diagram: Building Micro-Frontends Luca Mezzalira, 2021-11-17 What's the answer to today's increasingly complex web applications? Micro-frontends. Inspired by the microservices model, this approach lets you break interfaces into separate features managed by different teams of developers. With this practical guide, Luca Mezzalira shows software architects, tech leads, and software developers how to build and deliver artifacts atomically rather than use a big bang deployment. You'll learn how micro-frontends enable your team to choose any library or framework. This gives your organization technical flexibility and allows you to hire and retain a broad spectrum of talent. Micro-frontends also support distributed or colocated teams more efficiently. Pick up this book and learn how to get started with this technological breakthrough right away. Explore available frontend development architectures Learn how microservice principles apply to frontend development Understand the four pillars for creating a successful micro-frontend architecture Examine the benefits and pitfalls of existing micro-frontend architectures Learn principles and best practices for creating successful automation strategies Discover patterns for integrating micro-frontend architectures using microservices or a monolith API layer |
| chat gpt architecture diagram: Artificial Intelligence and Visualization: Advancing Visual Knowledge Discovery Boris Kovalerchuk, Kawa Nazemi, Răzvan Andonie, Nuno Datia, Ebad Bannissi, 2024 Zusammenfassung: This book continues a series of Springer publications devoted to the emerging field of Integrated Artificial Intelligence and Machine Learning with Visual Knowledge Discovery and Visual Analytics that combine advances in both fields. Artificial Intelligence and Machine Learning face long-standing challenges of explainability and interpretability that underpin trust. Such attributes are fundamental to both decision-making and knowledge discovery. Models are approximations and, at best, interpretations of reality that are transposed to algorithmic form. A visual explanation paradigm is critically important to address such challenges, as current studies demonstrate in salience analysis in deep learning for images and texts. Visualization means are generally effective for discovering and explaining high-dimensional patterns in all high-dimensional data, while preserving data properties and relations in visualizations is challenging. Recent developments, such as in General Line Coordinates, open new opportunities to address such challenges. This book contains extended papers presented in 2021 and 2022 at the International Conference on Information Visualization (IV) on AI and Visual Analytics, with 18 chapters from international collaborators. The book builds on the previous volume, published in 2022 in the Studies in Computational Intelligence. The current book focuses on the following themes: knowledge discovery with lossless visualizations, AI/ML through visual knowledge discovery with visual analytics case studies application, and visual knowledge discovery in text mining and natural language processing. The intended audience for this collection includes but is not limited to developers of emerging AI/machine learning and visualization applications, scientists, practitioners, and research students. It has multiple examples of the current integration of AI/machine learning and visualization for visual knowledge discovery, visual analytics, and text and natural language processing. The book provides case examples for future directions in this domain. New researchers find inspiration to join the profession of the field of AI/machine learning through a visualization lens. |
| chat gpt architecture diagram: Team Topologies Matthew Skelton, Manuel Pais, 2019-09-17 Effective software teams are essential for any organization to deliver value continuously and sustainably. But how do you build the best team organization for your specific goals, culture, and needs? Team Topologies is a practical, step-by-step, adaptive model for organizational design and team interaction based on four fundamental team types and three team interaction patterns. It is a model that treats teams as the fundamental means of delivery, where team structures and communication pathways are able to evolve with technological and organizational maturity. In Team Topologies, IT consultants Matthew Skelton and Manuel Pais share secrets of successful team patterns and interactions to help readers choose and evolve the right team patterns for their organization, making sure to keep the software healthy and optimize value streams. Team Topologies is a major step forward in organizational design for software, presenting a well-defined way for teams to interact and interrelate that helps make the resulting software architecture clearer and more sustainable, turning inter-team problems into valuable signals for the self-steering organization. |
| chat gpt architecture diagram: Machine Learning with PyTorch and Scikit-Learn Sebastian Raschka, Yuxi (Hayden) Liu, Vahid Mirjalili, 2022-02-25 This book of the bestselling and widely acclaimed Python Machine Learning series is a comprehensive guide to machine and deep learning using PyTorch s simple to code framework. Purchase of the print or Kindle book includes a free eBook in PDF format. Key Features Learn applied machine learning with a solid foundation in theory Clear, intuitive explanations take you deep into the theory and practice of Python machine learning Fully updated and expanded to cover PyTorch, transformers, XGBoost, graph neural networks, and best practices Book DescriptionMachine Learning with PyTorch and Scikit-Learn is a comprehensive guide to machine learning and deep learning with PyTorch. It acts as both a step-by-step tutorial and a reference you'll keep coming back to as you build your machine learning systems. Packed with clear explanations, visualizations, and examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, we teach the principles allowing you to build models and applications for yourself. Why PyTorch? PyTorch is the Pythonic way to learn machine learning, making it easier to learn and simpler to code with. This book explains the essential parts of PyTorch and how to create models using popular libraries, such as PyTorch Lightning and PyTorch Geometric. You will also learn about generative adversarial networks (GANs) for generating new data and training intelligent agents with reinforcement learning. Finally, this new edition is expanded to cover the latest trends in deep learning, including graph neural networks and large-scale transformers used for natural language processing (NLP). This PyTorch book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments.What you will learn Explore frameworks, models, and techniques for machines to learn from data Use scikit-learn for machine learning and PyTorch for deep learning Train machine learning classifiers on images, text, and more Build and train neural networks, transformers, and boosting algorithms Discover best practices for evaluating and tuning models Predict continuous target outcomes using regression analysis Dig deeper into textual and social media data using sentiment analysis Who this book is for If you have a good grasp of Python basics and want to start learning about machine learning and deep learning, then this is the book for you. This is an essential resource written for developers and data scientists who want to create practical machine learning and deep learning applications using scikit-learn and PyTorch. Before you get started with this book, you’ll need a good understanding of calculus, as well as linear algebra. |
| chat gpt architecture diagram: The Origin of Consciousness in the Breakdown of the Bicameral Mind Julian Jaynes, 2000-08-15 National Book Award Finalist: “This man’s ideas may be the most influential, not to say controversial, of the second half of the twentieth century.”—Columbus Dispatch At the heart of this classic, seminal book is Julian Jaynes's still-controversial thesis that human consciousness did not begin far back in animal evolution but instead is a learned process that came about only three thousand years ago and is still developing. The implications of this revolutionary scientific paradigm extend into virtually every aspect of our psychology, our history and culture, our religion—and indeed our future. “Don’t be put off by the academic title of Julian Jaynes’s The Origin of Consciousness in the Breakdown of the Bicameral Mind. Its prose is always lucid and often lyrical…he unfolds his case with the utmost intellectual rigor.”—The New York Times “When Julian Jaynes . . . speculates that until late in the twentieth millennium BC men had no consciousness but were automatically obeying the voices of the gods, we are astounded but compelled to follow this remarkable thesis.”—John Updike, The New Yorker “He is as startling as Freud was in The Interpretation of Dreams, and Jaynes is equally as adept at forcing a new view of known human behavior.”—American Journal of Psychiatry |
| chat gpt architecture diagram: Save Me a Seat (Scholastic Gold) Sarah Weeks, Gita Varadarajan, 2016-05-10 A new friend could be sitting right next to you. Save Me a Seat joins the Scholastic Gold line, which features award-winning and beloved novels. Includes exclusive bonus content!Joe and Ravi might be from very different places, but they're both stuck in the same place: SCHOOL.Joe's lived in the same town all his life, and was doing just fine until his best friends moved away and left him on his own. Ravi's family just moved to America from India, and he's finding it pretty hard to figure out where he fits in.Joe and Ravi don't think they have anything in common -- but soon enough they have a common enemy (the biggest bully in their class) and a common mission: to take control of their lives over the course of a single crazy week. |
| chat gpt architecture diagram: Modern Software Engineering David Farley, 2021-11-16 Improve Your Creativity, Effectiveness, and Ultimately, Your Code In Modern Software Engineering, continuous delivery pioneer David Farley helps software professionals think about their work more effectively, manage it more successfully, and genuinely improve the quality of their applications, their lives, and the lives of their colleagues. Writing for programmers, managers, and technical leads at all levels of experience, Farley illuminates durable principles at the heart of effective software development. He distills the discipline into two core exercises: learning and exploration and managing complexity. For each, he defines principles that can help you improve everything from your mindset to the quality of your code, and describes approaches proven to promote success. Farley's ideas and techniques cohere into a unified, scientific, and foundational approach to solving practical software development problems within realistic economic constraints. This general, durable, and pervasive approach to software engineering can help you solve problems you haven't encountered yet, using today's technologies and tomorrow's. It offers you deeper insight into what you do every day, helping you create better software, faster, with more pleasure and personal fulfillment. Clarify what you're trying to accomplish Choose your tools based on sensible criteria Organize work and systems to facilitate continuing incremental progress Evaluate your progress toward thriving systems, not just more legacy code Gain more value from experimentation and empiricism Stay in control as systems grow more complex Achieve rigor without too much rigidity Learn from history and experience Distinguish good new software development ideas from bad ones Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details. |
| chat gpt architecture diagram: Python Deep Learning Ivan Vasilev, 2023-11-24 Master effective navigation of neural networks, including convolutions and transformers, to tackle computer vision and NLP tasks using Python Key Features Understand the theory, mathematical foundations and structure of deep neural networks Become familiar with transformers, large language models, and convolutional networks Learn how to apply them to various computer vision and natural language processing problems Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionThe field of deep learning has developed rapidly recently and today covers a broad range of applications. This makes it challenging to navigate and hard to understand without solid foundations. This book will guide you from the basics of neural networks to the state-of-the-art large language models in use today. The first part of the book introduces the main machine learning concepts and paradigms. It covers the mathematical foundations, the structure, and the training algorithms of neural networks and dives into the essence of deep learning. The second part of the book introduces convolutional networks for computer vision. We’ll learn how to solve image classification, object detection, instance segmentation, and image generation tasks. The third part focuses on the attention mechanism and transformers – the core network architecture of large language models. We’ll discuss new types of advanced tasks they can solve, such as chatbots and text-to-image generation. By the end of this book, you’ll have a thorough understanding of the inner workings of deep neural networks. You'll have the ability to develop new models and adapt existing ones to solve your tasks. You’ll also have sufficient understanding to continue your research and stay up to date with the latest advancements in the field.What you will learn Establish theoretical foundations of deep neural networks Understand convolutional networks and apply them in computer vision applications Become well versed with natural language processing and recurrent networks Explore the attention mechanism and transformers Apply transformers and large language models for natural language and computer vision Implement coding examples with PyTorch, Keras, and Hugging Face Transformers Use MLOps to develop and deploy neural network models Who this book is for This book is for software developers/engineers, students, data scientists, data analysts, machine learning engineers, statisticians, and anyone interested in deep learning. Prior experience with Python programming is a prerequisite. |
| chat gpt architecture diagram: Conversational AI with Rasa Xiaoquan Kong, Guan Wang, Alan Nichol, 2021-10-08 Create next-level AI assistants and transform how customers communicate with businesses with the power of natural language understanding and dialogue management using Rasa Key FeaturesUnderstand the architecture and put the underlying principles of the Rasa framework to practiceLearn how to quickly build different types of chatbots such as task-oriented, FAQ-like, and knowledge graph-based chatbotsExplore best practices for working with Rasa and its debugging and optimizing aspectsBook Description The Rasa framework enables developers to create industrial-strength chatbots using state-of-the-art natural language processing (NLP) and machine learning technologies quickly, all in open source. Conversational AI with Rasa starts by showing you how the two main components at the heart of Rasa work – Rasa NLU (natural language understanding) and Rasa Core. You'll then learn how to build, configure, train, and serve different types of chatbots from scratch by using the Rasa ecosystem. As you advance, you'll use form-based dialogue management, work with the response selector for chitchat and FAQ-like dialogs, make use of knowledge base actions to answer questions for dynamic queries, and much more. Furthermore, you'll understand how to customize the Rasa framework, use conversation-driven development patterns and tools to develop chatbots, explore what your bot can do, and easily fix any mistakes it makes by using interactive learning. Finally, you'll get to grips with deploying the Rasa system to a production environment with high performance and high scalability and cover best practices for building an efficient and robust chat system. By the end of this book, you'll be able to build and deploy your own chatbots using Rasa, addressing the common pain points encountered in the chatbot life cycle. What you will learnUse the response selector to handle chitchat and FAQsCreate custom actions using the Rasa SDKTrain Rasa to handle complex named entity recognitionBecome skilled at building custom components in the Rasa frameworkValidate and test dialogs end to end in RasaDevelop and refine a chatbot system by using conversation-driven deployment processingUse TensorBoard for tuning to find the best configuration optionsDebug and optimize dialogue systems based on RasaWho this book is for This book is for NLP professionals as well as machine learning and deep learning practitioners who have knowledge of natural language processing and want to build chatbots with Rasa. Anyone with beginner-level knowledge of NLP and deep learning will be able to get the most out of the book. |
| chat gpt architecture diagram: Introduction to Evolutionary Computing A.E. Eiben, J.E. Smith, 2007-08-06 The first complete overview of evolutionary computing, the collective name for a range of problem-solving techniques based on principles of biological evolution, such as natural selection and genetic inheritance. The text is aimed directly at lecturers and graduate and undergraduate students. It is also meant for those who wish to apply evolutionary computing to a particular problem or within a given application area. The book contains quick-reference information on the current state-of-the-art in a wide range of related topics, so it is of interest not just to evolutionary computing specialists but to researchers working in other fields. |
| chat gpt architecture diagram: Designing Bots Amir Shevat, 2017-05-17 From Facebook Messenger to Kik, and from Slack bots to Google Assistant, Amazon Alexa, and email bots, the new conversational apps are revolutionizing the way we interact with software. This practical guide shows you how to design and build great conversational experiences and delightful bots that help people be more productive, whether it’s for a new consumer service or an enterprise efficiency product. Ideal for designers, product managers, and entrepreneurs, this book explores what works and what doesn’t in real-world bot examples, and provides practical design patterns for your bot-building toolbox. You’ll learn how to use an effective onboarding process, outline different flows, define a bot personality, and choose the right balance of rich control and text. Explore different bot use-cases and design best practices Understand bot anatomy—such as brand and personality, conversations, advanced UI controls—and their associated design patterns Learn steps for building a Facebook Messenger consumer bot and a Slack business bot Explore the lessons learned and shared experiences of designers and entrepreneurs who have built bots Design and prototype your first bot, and experiment with user feedback |
| chat gpt architecture diagram: Speech & Language Processing Dan Jurafsky, 2000-09 |
| chat gpt architecture diagram: Efficient Processing of Deep Neural Networks Vivienne Sze, Yu-Hsin Chen, Tien-Ju Yang, Joel S. Emer, 2022-05-31 This book provides a structured treatment of the key principles and techniques for enabling efficient processing of deep neural networks (DNNs). DNNs are currently widely used for many artificial intelligence (AI) applications, including computer vision, speech recognition, and robotics. While DNNs deliver state-of-the-art accuracy on many AI tasks, it comes at the cost of high computational complexity. Therefore, techniques that enable efficient processing of deep neural networks to improve key metrics—such as energy-efficiency, throughput, and latency—without sacrificing accuracy or increasing hardware costs are critical to enabling the wide deployment of DNNs in AI systems. The book includes background on DNN processing; a description and taxonomy of hardware architectural approaches for designing DNN accelerators; key metrics for evaluating and comparing different designs; features of DNN processing that are amenable to hardware/algorithm co-design to improve energy efficiency and throughput; and opportunities for applying new technologies. Readers will find a structured introduction to the field as well as formalization and organization of key concepts from contemporary work that provide insights that may spark new ideas. |
| chat gpt architecture diagram: Transformers for Natural Language Processing Denis Rothman, 2021-01-29 Publisher's Note: A new edition of this book is out now that includes working with GPT-3 and comparing the results with other models. It includes even more use cases, such as casual language analysis and computer vision tasks, as well as an introduction to OpenAI's Codex. Key FeaturesBuild and implement state-of-the-art language models, such as the original Transformer, BERT, T5, and GPT-2, using concepts that outperform classical deep learning modelsGo through hands-on applications in Python using Google Colaboratory Notebooks with nothing to install on a local machineTest transformer models on advanced use casesBook Description The transformer architecture has proved to be revolutionary in outperforming the classical RNN and CNN models in use today. With an apply-as-you-learn approach, Transformers for Natural Language Processing investigates in vast detail the deep learning for machine translations, speech-to-text, text-to-speech, language modeling, question answering, and many more NLP domains with transformers. The book takes you through NLP with Python and examines various eminent models and datasets within the transformer architecture created by pioneers such as Google, Facebook, Microsoft, OpenAI, and Hugging Face. The book trains you in three stages. The first stage introduces you to transformer architectures, starting with the original transformer, before moving on to RoBERTa, BERT, and DistilBERT models. You will discover training methods for smaller transformers that can outperform GPT-3 in some cases. In the second stage, you will apply transformers for Natural Language Understanding (NLU) and Natural Language Generation (NLG). Finally, the third stage will help you grasp advanced language understanding techniques such as optimizing social network datasets and fake news identification. By the end of this NLP book, you will understand transformers from a cognitive science perspective and be proficient in applying pretrained transformer models by tech giants to various datasets. What you will learnUse the latest pretrained transformer modelsGrasp the workings of the original Transformer, GPT-2, BERT, T5, and other transformer modelsCreate language understanding Python programs using concepts that outperform classical deep learning modelsUse a variety of NLP platforms, including Hugging Face, Trax, and AllenNLPApply Python, TensorFlow, and Keras programs to sentiment analysis, text summarization, speech recognition, machine translations, and moreMeasure the productivity of key transformers to define their scope, potential, and limits in productionWho this book is for Since the book does not teach basic programming, you must be familiar with neural networks, Python, PyTorch, and TensorFlow in order to learn their implementation with Transformers. Readers who can benefit the most from this book include experienced deep learning & NLP practitioners and data analysts & data scientists who want to process the increasing amounts of language-driven data. |
| chat gpt architecture diagram: SYSMOD - The Systems Modeling Toolbox - Pragmatic MBSE with SysML Tim Weilkiens, 2016 SYSMOD is an MBSE toolbox for pragmatic modeling of systems. It is well-suited to be used with SysML. The book provides a set of methods with roles and outputs. Concrete guidances and examples show how to apply the methods with SysML. * Requirements modeling * System Context * Use Cases * Functional, Physical, Logical and Product Architectures * Guidances how to create a SysML model * Full-fledged SysML example * Complete definition of a profile for SYSMOD This book is also available as an eBook at leanpub.com/sysmod. |
| chat gpt architecture diagram: The Plot Dot Derek Murphy, 2016-04-13 An Adult Coloring Book for Authors!This book will help you paint powerful visual scenes that stick with readers long after they're finished your book. The techniques described within are simple and easy to use. Think of this as a guided coloring book adventure. YOUR adventure.There are lots of systems and guides to plotting, but if you're like me you have journals filled with notes, scenes and description... it can get overwhelming. I've based this book on traditional three-act story architecture, but it's greatly simplified. Plotters and pantsers can use the guided exercises to gain greater visual clarity and build more meaningful scenes with resonance. This book will help you get organized and unlock hidden potential in your scenes that you didn't know was there, by going beyond words and focusing on drawing and coloring your scenes until you have a full outline.This book makes an excellent workbook for writing retreats, is simple enough for children to use (it's never too early to write your first novel), and introduces a new, and hopefully useful, way to organize your novel, improve your writing, and create unforgettable scenes that will make a deep and lasting impact. |
| chat gpt architecture diagram: A Philosophy of Software Design John K. Ousterhout, 2021 This book addresses the topic of software design: how to decompose complex software systems into modules (such as classes and methods) that can be implemented relatively independently. The book first introduces the fundamental problem in software design, which is managing complexity. It then discusses philosophical issues about how to approach the software design process and it presents a collection of design principles to apply during software design. The book also introduces a set of red flags that identify design problems. You can apply the ideas in this book to minimize the complexity of large software systems, so that you can write software more quickly and cheaply.--Amazon. |
| chat gpt architecture diagram: Embedded Android Karim Yaghmour, 2013-03-15 Embedded Android is for Developers wanting to create embedded systems based on Android and for those wanting to port Android to new hardware, or creating a custom development environment. Hackers and moders will also find this an indispensible guide to how Android works. |
| chat gpt architecture diagram: The SaaS Sales Method Fernando Pizarro, Winning by Winning by Design, Dominique Levin, Dan Smith, Jacco Van Der Kooij, 2021-07-29 In a modern recurring revenue business it is impossible to scale without treating sales as a science. In this first book of the Sales Blueprints series, Jacco Van Der Kooij and Fernando Pizarro break down the science of sales into its basic elements. Unlike any book before it, The SaaS Sales Method exposes the math the underpins each stage in revenue production, from marketing, to sales, to customer success, and infers how revenue leaders should structure their processes, organizations, and training in each.By linking all three functions, The SaaS Sales Method provides a framework for the modern revenue leader to understand and improve their entire system, shifting from what the authors call a superstar culture to a science culture in the process.While subsequent books in the series go into depth on the specifics of each revenue function and the skills needed to succeed in each, The SaaS Sales Method is the glue that holds the entire approach together. |
| chat gpt architecture diagram: Biomimicry Janine M. Benyus, 2009-08-11 Repackaged with a new afterword, this valuable and entertaining (New York Times Book Review) book explores how scientists are adapting nature's best ideas to solve tough 21st century problems. Biomimicry is rapidly transforming life on earth. Biomimics study nature's most successful ideas over the past 3.5 million years, and adapt them for human use. The results are revolutionizing how materials are invented and how we compute, heal ourselves, repair the environment, and feed the world. Janine Benyus takes readers into the lab and in the field with maverick thinkers as they: discover miracle drugs by watching what chimps eat when they're sick; learn how to create by watching spiders weave fibers; harness energy by examining how a leaf converts sunlight into fuel in trillionths of a second; and many more examples. Composed of stories of vision and invention, personalities and pipe dreams, Biomimicry is must reading for anyone interested in the shape of our future. |
| chat gpt architecture diagram: Neural Networks and Machine Learning Christopher Bishop, 1998-10-20 In recent years neural computing has emerged as a practical technology, with successful applications in many fields. The majority of these applications are concerned with problems in pattern recognition, and make use of feedforward network architectures such as the multilayer perceptron and the radial basis function network. Also, it has become widely acknowledged that successful applications of neural computing require a principled, rather than ad hoc, approach. (From the preface to Neural Networks for Pattern Recognition by C.M. Bishop, Oxford Univ Press 1995.) This NATO volume, based on a 1997 workshop, presents a coordinated series of tutorial articles covering recent developments in the field of neural computing. It is ideally suited to graduate students and researchers. |
| chat gpt architecture diagram: Architectural Diagrams Mi Young Pyo, 2015 The trendsetting architect Rem Koolhaas has carried it out to perfection, whereas the next generation of international stars refined it even more, giving us the unconventional presentation of designs and ideas in the form of diagrams. This method of presentation is easy to understand when dealing with the client and can be communicated internationally, beyond language and cultural barriers - a product of our globalised world. However, diagrams are now much more than explanations and form their own discipline in creative professions connected to design and construction. What looks simple is in fact a complex matter. This title in the series Construction and Design Manual is in its second edition and assembles 384 pages of diagrams by avant-garde architects and designers who specialise in public space, landscape architecture and urban planning. |
| chat gpt architecture diagram: Periodization Tudor O. Bompa, G. Gregory Haff, 2018-04-24 Learn how to train for maximum gains with Periodization: Theory and Methodology of Training. Guided by the expertise of Tudor O. Bompa, the pioneer of periodization training, and leading periodization researcher G. Gregory Haff, you’ll learn how to vary the intensity and volume of training to optimize the body’s ability to recover and rebuild—resulting in better performance and less risk of injury. Translated into nine languages, Periodization: Theory and Methodology of Training has become one of the major resources on periodization for sport scientists, coaches, and athletes throughout the world. Since the introduction of this groundbreaking training theory by Tudor O. Bompa in 1963, periodization has become the basis of every serious athlete’s training. Now in its fifth edition, Bompa’s classic text combines the concepts central to periodization and training theory with contemporary advances in sport science, physiology, and coaching. No other text discusses planning and periodization in such detail or with so many specific, practical examples from a variety of sports. With the fifth edition of Periodization, you can learn the principles, objectives, and components of a successful long-term training program and how to plan the right program to achieve your performance goals. Periodization also contains proven strategies for optimal peaking and specifics on training for better motor ability, working capacity, skill effectiveness, and psychological adaptability. Better organized and easier to read, the fifth edition of this definitive text presents the latest refinements to periodization theory: • New research on rest and restoration, specifically countermeasures used in facilitating recovery plus practical suggestions for implementation • How the use of sequential training and delayed training effects can produce optimal performance at major competitions • A comprehensive discussion, grounded in scientific data, on applying various methods of peaking to ensure optimal performance for competition • New information on how concepts such as conjugated sequencing and summated microcycle structures can maximize strength gains and direct training • Expanded information on the development of sport performance characteristics, including speed training and sport-specific endurance Chapter summaries will help you review and reference major concepts throughout the text. Plan and document each training program with ease using the duplication-ready annual and four-year training plan charts included in the appendix. When it comes to designing programs for optimal training, Tudor Bompa’s expertise is unmatched. The fifth edition of Periodization: Theory and Methodology of Training presents the latest refinements to Bompa’s periodization theory to help you create training programs that enhance sport skills and ensure peak performance. |
| chat gpt architecture diagram: Deep Learning and the Game of Go Kevin Ferguson, Max Pumperla, 2019-01-06 Summary Deep Learning and the Game of Go teaches you how to apply the power of deep learning to complex reasoning tasks by building a Go-playing AI. After exposing you to the foundations of machine and deep learning, you'll use Python to build a bot and then teach it the rules of the game. Foreword by Thore Graepel, DeepMind Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology The ancient strategy game of Go is an incredible case study for AI. In 2016, a deep learning-based system shocked the Go world by defeating a world champion. Shortly after that, the upgraded AlphaGo Zero crushed the original bot by using deep reinforcement learning to master the game. Now, you can learn those same deep learning techniques by building your own Go bot! About the Book Deep Learning and the Game of Go introduces deep learning by teaching you to build a Go-winning bot. As you progress, you'll apply increasingly complex training techniques and strategies using the Python deep learning library Keras. You'll enjoy watching your bot master the game of Go, and along the way, you'll discover how to apply your new deep learning skills to a wide range of other scenarios! What's inside Build and teach a self-improving game AI Enhance classical game AI systems with deep learning Implement neural networks for deep learning About the Reader All you need are basic Python skills and high school-level math. No deep learning experience required. About the Author Max Pumperla and Kevin Ferguson are experienced deep learning specialists skilled in distributed systems and data science. Together, Max and Kevin built the open source bot BetaGo. Table of Contents PART 1 - FOUNDATIONS Toward deep learning: a machine-learning introduction Go as a machine-learning problem Implementing your first Go bot PART 2 - MACHINE LEARNING AND GAME AI Playing games with tree search Getting started with neural networks Designing a neural network for Go data Learning from data: a deep-learning bot Deploying bots in the wild Learning by practice: reinforcement learning Reinforcement learning with policy gradients Reinforcement learning with value methods Reinforcement learning with actor-critic methods PART 3 - GREATER THAN THE SUM OF ITS PARTS AlphaGo: Bringing it all together AlphaGo Zero: Integrating tree search with reinforcement learning |
| chat gpt architecture diagram: Introduction to Grid Computing Bart Jacob, 2005 |
| chat gpt architecture diagram: Dictionary of Acronyms and Technical Abbreviations Jakob Vlietstra, 2012-12-06 This Dictionary covers information and communication technology (ICT), including hardware and software; information networks, including the Internet and the World Wide Web; automatic control; and ICT-related computer-aided fields. The Dictionary also lists abbreviated names of relevant organizations, conferences, symposia and workshops. This reference is important for all practitioners and users in the areas mentioned above, and those who consult or write technical material. This Second Edition contains 10,000 new entries, for a total of 33,000. |
| chat gpt architecture diagram: Getting Ready for Model 3 Roger Pressman, 2016-06-01 |
| chat gpt architecture diagram: Interpretable Machine Learning with Python Serg Masís, 2021-03-26 A deep and detailed dive into the key aspects and challenges of machine learning interpretability, complete with the know-how on how to overcome and leverage them to build fairer, safer, and more reliable models Key Features Learn how to extract easy-to-understand insights from any machine learning model Become well-versed with interpretability techniques to build fairer, safer, and more reliable models Mitigate risks in AI systems before they have broader implications by learning how to debug black-box models Book DescriptionDo you want to gain a deeper understanding of your models and better mitigate poor prediction risks associated with machine learning interpretation? If so, then Interpretable Machine Learning with Python deserves a place on your bookshelf. We’ll be starting off with the fundamentals of interpretability, its relevance in business, and exploring its key aspects and challenges. As you progress through the chapters, you'll then focus on how white-box models work, compare them to black-box and glass-box models, and examine their trade-off. You’ll also get you up to speed with a vast array of interpretation methods, also known as Explainable AI (XAI) methods, and how to apply them to different use cases, be it for classification or regression, for tabular, time-series, image or text. In addition to the step-by-step code, this book will also help you interpret model outcomes using examples. You’ll get hands-on with tuning models and training data for interpretability by reducing complexity, mitigating bias, placing guardrails, and enhancing reliability. The methods you’ll explore here range from state-of-the-art feature selection and dataset debiasing methods to monotonic constraints and adversarial retraining. By the end of this book, you'll be able to understand ML models better and enhance them through interpretability tuning. What you will learn Recognize the importance of interpretability in business Study models that are intrinsically interpretable such as linear models, decision trees, and Naïve Bayes Become well-versed in interpreting models with model-agnostic methods Visualize how an image classifier works and what it learns Understand how to mitigate the influence of bias in datasets Discover how to make models more reliable with adversarial robustness Use monotonic constraints to make fairer and safer models Who this book is for This book is primarily written for data scientists, machine learning developers, and data stewards who find themselves under increasing pressures to explain the workings of AI systems, their impacts on decision making, and how they identify and manage bias. It’s also a useful resource for self-taught ML enthusiasts and beginners who want to go deeper into the subject matter, though a solid grasp on the Python programming language and ML fundamentals is needed to follow along. |
| chat gpt architecture diagram: 2009 IEEE Conference on Computer Vision and Pattern Recognition IEEE Staff, 2009 |
| chat gpt architecture diagram: Azure for .NET Core Developers Kasam Ahmed Shaikh, 2020-04-14 Demystifying working in Azure Ecosystem for .NET Core DevelopersÊ KEY FEATURES - Discover and put to use the latest features in .NET Core 3.1 and Azure Functions V3Ê - Learn how to debugging Azure Functions from Production, hosted on CloudÊ - Understand the working of Application Key Management with Security aspects DESCRIPTIONÊ Every developer is striving hard to up-skill oneself from a developer to a Cloud developer and with the growing pace of cloud programming, this up-gradation is not simple.Ê This book will help .NET Core developers to seamlessly cover this said journey. It covers the newly released .Net Core 3.0 / 3.1 features including, Azure Function V3. The book not only focusses on one way of working with Azure Cloud services but also includes another viable way of managing Azure resources with the software application. The book also touches base on some Azure products and services. From exploring the most used Azure services to touching the newest version of offerings, this book is aimed to cover everything from a developer perspective. Code exercise, Code blocks, azure service implementation, application secrets keys management, free super-fast hosting options along with live debugging of code hosted on Cloud, are some of the key take-aways from this book.Ê Ê WHAT WILL YOU LEARN - Develop a .NET core application with Azure App serviceÊ - Use Azure CosmosDB to manage database services - Explore & work with Microsoft Azure StorageÊ - Able to have the best hosting option for Static Content web applicationÊ - Work with Azure Functions V3 using Visual Studio 2019Ê - Implement best Key Management in the app on Azure WHO THIS BOOK IS FORÊ This book is for aspiring Cloud developers with some experience in Microsoft cloud services. It is also for .NET Core developers who wish to learn and use Azure solutions.Ê TABLE OF CONTENTS 1. Azure Ecosystem 2. My App on Cloud Ð Microsoft Azure 3. Application Backend - Azure CosmosDB 4. Working with Microsoft Azure Storage 5. Working with Microsoft Azure Storage as Hosting option 6. Securing Application secrets keys with Azure 7. Step towards Serverless approach |
| chat gpt architecture diagram: Software Architecture in Practice Len Bass, Paul Clements, Rick Kazman, 2003 This is the eagerly-anticipated revision to one of the seminal books in the field of software architecture which clearly defines and explains the topic. |
| chat gpt architecture diagram: Designing Solutions for Your Business Problems Betty Vandenbosch, 2003-10-27 Designing Solutions for Your Business Problems is an essential resource for managers and consultants who help organizations resolve ambiguous problems and develop new opportunities. Taking a hands-on, practical approach, Betty Vandenbosch—a leading management consultant and educator—outlines the details on how to conduct a proven process for designing solutions. Designing Solutions for Your Business Problems will teach you how to curtail investigation and generate and justify ideas without sacrificing thoroughness, creativity, persuasiveness, and fit. You will be able to capitalize on more opportunities, and your problem-solving skills will become more efficient and your solutions more compelling. This book will help you design better solutions and design them faster. Betty Vandenbosch offers a variety of useful techniques such as the scooping diagram, which provides a framework for action, and the logic diagram, which tests the validity of a potential solution. In addition, the book contains illustrative real-life examples of the Designing Solutions approach from a variety of organizations. |
| chat gpt architecture diagram: Clean Code Robert C. Martin, 2009 This title shows the process of cleaning code. Rather than just illustrating the end result, or just the starting and ending state, the author shows how several dozen seemingly small code changes can positively impact the performance and maintainability of an application code base. |
| chat gpt architecture diagram: The Digital Doctor: Hope, Hype, and Harm at the Dawn of Medicine’s Computer Age Robert Wachter, 2015-04-10 The New York Times Science Bestseller from Robert Wachter, Modern Healthcare’s #1 Most Influential Physician-Executive in the US While modern medicine produces miracles, it also delivers care that is too often unsafe, unreliable, unsatisfying, and impossibly expensive. For the past few decades, technology has been touted as the cure for all of healthcare’s ills. But medicine stubbornly resisted computerization – until now. Over the past five years, thanks largely to billions of dollars in federal incentives, healthcare has finally gone digital. Yet once clinicians started using computers to actually deliver care, it dawned on them that something was deeply wrong. Why were doctors no longer making eye contact with their patients? How could one of America’s leading hospitals give a teenager a 39-fold overdose of a common antibiotic, despite a state-of-the-art computerized prescribing system? How could a recruiting ad for physicians tout the absence of an electronic medical record as a major selling point? Logically enough, we’ve pinned the problems on clunky software, flawed implementations, absurd regulations, and bad karma. It was all of those things, but it was also something far more complicated. And far more interesting . . . Written with a rare combination of compelling stories and hard-hitting analysis by one of the nation’s most thoughtful physicians, The Digital Doctor examines healthcare at the dawn of its computer age. It tackles the hard questions, from how technology is changing care at the bedside to whether government intervention has been useful or destructive. And it does so with clarity, insight, humor, and compassion. Ultimately, it is a hopeful story. We need to recognize that computers in healthcare don’t simply replace my doctor’s scrawl with Helvetica 12, writes the author Dr. Robert Wachter. Instead, they transform the work, the people who do it, and their relationships with each other and with patients. . . . Sure, we should have thought of this sooner. But it’s not too late to get it right. This riveting book offers the prescription for getting it right, making it essential reading for everyone – patient and provider alike – who cares about our healthcare system. |
| chat gpt architecture diagram: Outside in Harley Manning, Kerry Bodine, 2012 For readers of Delivering Happiness and The New Gold Standard--a revolutionary approach to understanding and mastering the customer experience from Forrester Research. |
| chat gpt architecture diagram: Our Final Invention James Barrat, 2013-10-01 Elon Musk named Our Final Invention one of five books everyone should read about the future—a Huffington Post Definitive Tech Book of 2013. Artificial Intelligence helps choose what books you buy, what movies you see, and even who you date. It puts the “smart” in your smartphone and soon it will drive your car. It makes most of the trades on Wall Street, and controls vital energy, water, and transportation infrastructure. But Artificial Intelligence can also threaten our existence. In as little as a decade, AI could match and then surpass human intelligence. Corporations and government agencies are pouring billions into achieving AI’s Holy Grail—human-level intelligence. Once AI has attained it, scientists argue, it will have survival drives much like our own. We may be forced to compete with a rival more cunning, more powerful, and more alien than we can imagine. Through profiles of tech visionaries, industry watchdogs, and groundbreaking AI systems, Our Final Invention explores the perils of the heedless pursuit of advanced AI. Until now, human intelligence has had no rival. Can we coexist with beings whose intelligence dwarfs our own? And will they allow us to? “If you read just one book that makes you confront scary high-tech realities that we’ll soon have no choice but to address, make it this one.” —The Washington Post “Science fiction has long explored the implications of humanlike machines (think of Asimov’s I, Robot), but Barrat’s thoughtful treatment adds a dose of reality.” —Science News “A dark new book . . . lays out a strong case for why we should be at least a little worried.” —The New Yorker |
| chat gpt architecture diagram: TOGAF® Version 9.1 The Open Group, 2011-01-01 For trainers free additional material of this book is available. This can be found under the Training Material tab. Log in with your trainer account to access the material. TOGAF is a framework - a detailed method and a set of supporting tools - for developing an enterprise architecture, developed by members of The Open Group Architecture Forum. TOGAF Version 9.1 is a maintenance update to TOGAF 9, addressing comments raised since the introduction of TOGAF 9 in 2009. It retains the major features and structure of TOGAF 9, thereby preserving existing investment in TOGAF, and adds further detail and clarification to what is already proven.It may be used freely by any organization wishing to develop an enterprise architecture for use within that organization (subject to the Conditions of Use). This Book is divided into seven parts: Part I - Introduction This part provides a high-level introduction to the key concepts of enterprise architecture and in particular the TOGAF approach. It contains the definitions of terms used throughout TOGAF and release notes detailing the changes between this version and the previous version of TOGAF. Part II - Architecture Development Method This is the core of TOGAF. It describes the TOGAF Architecture Development Method (ADM) a step-by-step approach to developing an enterprise architecture. Part III - ADM Guidelines & Techniques This part contains a collection of guidelines and techniques available for use in applying TOGAF and the TOGAF ADM. Part IV - Architecture Content Framework This part describes the TOGAF content framework, including a structured metamodel for architectural artifacts, the use of re-usable architecture building blocks, and an overview of typical architecture deliverables. Part V - Enterprise Continuum & Tools This part discusses appropriate taxonomies and tools to categorize and store the outputs of architecture activity within an enterprise. Part VI - TOGAF Reference Models This part provides a selection of architectural reference models, which includes the TOGAF Foundation Architecture, and the Integrated Information Infrastructure Reference Model (III-RM). Part VII Architecture Capability Framework This section looks at roles, Governance, compliance skills and much more practical guidance |
d $ ä1$M g Mä y ¥]o M ä È7ÓMgë Óä¥ ] - intuitionlabs.ai
ChatGPT is a conversational AI system built on large language model (LLM) technology – essentially a ** Generative AI** that produces human-like text responses from prompts. It was …
The ChatGPT Architecture: An In-Depth Exploration of …
Figure 4-2 presents an architecture diagram of ChatGPT, illustrating its training process in detail. This diagram provides a comprehensive view of how ChatGPT learns and refines its …
POV ChatGPT - Mindtree
ChatGPT is a powerful text-generating dialogue system. It is a natural language processing model (NLP) that generates humanlike responses to inputs from users. Based on Generative Pre …
How ChatGPT Works - Amazon Web Services
ChatGPT is built on GPT, one of the largest Transformer models commercially available. Neural networks simply predict outputs based on inputs. To create a chatbot then, you would need to …
ChatGPT: The Evolution of Natural Language Processing
provides details on the architecture of ChatGPT, how to train and fine-tune the model for specific applications, as well as the potential applications of ChatGPT in fields such as consulting, …
CHATGPT - Formatio
L'architecture transformateur, présentée dans un article publié par Vaswani et al. en 2017 "Attention Is All You Need", est un type d'architecture de réseau neuronal particulièrement …
Chat GPT - Solution Architecture v. 1 - Estafet
Architecture design assistance: ChatGPT can provide suggestions for architecture patterns, recommend best practices and perform trade-off analysis of different design options. This …
ChatGPT: The Task - University of Waterloo
The Core Task of ChatGPT Given a text prompt, predict the natural language token (word) that comes next. ChatGPT is a Large Language Model powered by a deep Artificial Neural …
ChatGPT: Applications, Opportunities, and Threats - arXiv.org
The architecture known as GPT, initially introduced by OpenAI in 2018, serves as the basis for ChatGPT. The first version, GPT-1, had 117 million parameters to work with and was trained …
ChatGPT For Dummies
ChatGPT utilizes natural-language processing and is built on OpenAI's generative pretrained transformer (GPT) architecture. It understands context and language, which allows for …
ChatGPT: Optimizing Language Models for Dialogue - GitHub …
A diagram illustrating the three steps of InstructGPT: (1) supervised fine-tuning (SFT), (2) reward model (RM) training, and (3) reinforcement learning via proximal policy optimization (PPO). …
CHAT GPT: EXPLORING THE CAPABILITIES AND LIMITATIONS …
The GPT (Generative Pre-training Transformer) architecture is the name of the transformer architecture that ChatGPT employs. It can produce high-quality content in a variety of …
Systematic exploration and in-depth analysis of ChatGPT
This study paper dives into this LLM with a case study of ChatGPT’s architecture and provides a thorough comparative analysis of its numerous versions, tracking its his-tory from its …
ChatGPT: A Review of Capabilities, Challenges, and Future …
A significant development in the field of natural language processing (NLP) is ChatGPT, created by OpenAI. The model, which is based on the Generative Pre-trained Transformer (GPT) …
Build up your own ChatGPT Environment with Azure OpenAI …
It then provides some architecture information on internal ChatGPT environment with Azure OpenAI platform & Azure AI search services, explaining what they are, how they work together.
Prompt Engineering For ChatGPT: A Quick Guide To …
To fully harness the potential of ChatGPT, it is crucial to understand and master the art of prompt engineering—the process of designing and refining input prompts to elicit desired responses …
Emerging Technologies ChatGPT for business - Fujitsu Global
ChatGPT is based on the ‘Generative Pretrained Transformer (GPT)’ architecture. Generative means it creates a synthesised output or output options. It is not an ‘if-then’ system where a …
What is ChatGPT? - AlphaPlus
It is built on an architecture that mimics the human brain called the GPT (Generative Pre-trained Transformer) model. The GPT architecture allows ChatGPT to generate natural language text …
Gen AI LLM - A new era of generative AI for everyone
ChatGPT has woken up the world to the transformative potential of artificial intelligence (AI), capturing global attention and sparking a wave of creativity rarely seen before. Its ability to …
ChatGPT for PLC/DCS Control Logic Generation - arXiv.org
We tested the prompts by generating answers with ChatGPT using the GPT-4 LLM. It generated syntactically correct IEC 61131-3 Structured Text code in many cases and demonstrated …
d $ ä1$M g Mä y ¥]o M ä È7ÓMgë Óä¥ ] - intuitionlabs.ai
ChatGPT is a conversational AI system built on large language model (LLM) technology – essentially a ** Generative AI** that produces human-like text responses from prompts. It was …
The ChatGPT Architecture: An In-Depth Exploration of …
Figure 4-2 presents an architecture diagram of ChatGPT, illustrating its training process in detail. This diagram provides a comprehensive view of how ChatGPT learns and refines its …
POV ChatGPT - Mindtree
ChatGPT is a powerful text-generating dialogue system. It is a natural language processing model (NLP) that generates humanlike responses to inputs from users. Based on Generative Pre …
How ChatGPT Works - Amazon Web Services
ChatGPT is built on GPT, one of the largest Transformer models commercially available. Neural networks simply predict outputs based on inputs. To create a chatbot then, you would need to …
ChatGPT: The Evolution of Natural Language Processing
provides details on the architecture of ChatGPT, how to train and fine-tune the model for specific applications, as well as the potential applications of ChatGPT in fields such as consulting, …
CHATGPT - Formatio
L'architecture transformateur, présentée dans un article publié par Vaswani et al. en 2017 "Attention Is All You Need", est un type d'architecture de réseau neuronal particulièrement …
Chat GPT - Solution Architecture v. 1 - Estafet
Architecture design assistance: ChatGPT can provide suggestions for architecture patterns, recommend best practices and perform trade-off analysis of different design options. This …
ChatGPT: The Task - University of Waterloo
The Core Task of ChatGPT Given a text prompt, predict the natural language token (word) that comes next. ChatGPT is a Large Language Model powered by a deep Artificial Neural …
ChatGPT: Applications, Opportunities, and Threats - arXiv.org
The architecture known as GPT, initially introduced by OpenAI in 2018, serves as the basis for ChatGPT. The first version, GPT-1, had 117 million parameters to work with and was trained …
ChatGPT For Dummies
ChatGPT utilizes natural-language processing and is built on OpenAI's generative pretrained transformer (GPT) architecture. It understands context and language, which allows for …
ChatGPT: Optimizing Language Models for Dialogue
A diagram illustrating the three steps of InstructGPT: (1) supervised fine-tuning (SFT), (2) reward model (RM) training, and (3) reinforcement learning via proximal policy optimization (PPO). …
CHAT GPT: EXPLORING THE CAPABILITIES AND …
The GPT (Generative Pre-training Transformer) architecture is the name of the transformer architecture that ChatGPT employs. It can produce high-quality content in a variety of …
Systematic exploration and in-depth analysis of ChatGPT
This study paper dives into this LLM with a case study of ChatGPT’s architecture and provides a thorough comparative analysis of its numerous versions, tracking its his-tory from its …
ChatGPT: A Review of Capabilities, Challenges, and Future …
A significant development in the field of natural language processing (NLP) is ChatGPT, created by OpenAI. The model, which is based on the Generative Pre-trained Transformer (GPT) …
Build up your own ChatGPT Environment with Azure OpenAI …
It then provides some architecture information on internal ChatGPT environment with Azure OpenAI platform & Azure AI search services, explaining what they are, how they work together.
Prompt Engineering For ChatGPT: A Quick Guide To …
To fully harness the potential of ChatGPT, it is crucial to understand and master the art of prompt engineering—the process of designing and refining input prompts to elicit desired responses …
Emerging Technologies ChatGPT for business - Fujitsu Global
ChatGPT is based on the ‘Generative Pretrained Transformer (GPT)’ architecture. Generative means it creates a synthesised output or output options. It is not an ‘if-then’ system where a …
What is ChatGPT? - AlphaPlus
It is built on an architecture that mimics the human brain called the GPT (Generative Pre-trained Transformer) model. The GPT architecture allows ChatGPT to generate natural language text …
Gen AI LLM - A new era of generative AI for everyone
ChatGPT has woken up the world to the transformative potential of artificial intelligence (AI), capturing global attention and sparking a wave of creativity rarely seen before. Its ability to …
ChatGPT for PLC/DCS Control Logic Generation - arXiv.org
We tested the prompts by generating answers with ChatGPT using the GPT-4 LLM. It generated syntactically correct IEC 61131-3 Structured Text code in many cases and demonstrated …
