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| large language models 101: Large Language Models John Atkinson-Abutridy, 2024-10-17 This book serves as an introduction to the science and applications of Large Language Models (LLMs). You'll discover the common thread that drives some of the most revolutionary recent applications of artificial intelligence (AI): from conversational systems like ChatGPT or BARD, to machine translation, summary generation, question answering, and much more. At the heart of these innovative applications is a powerful and rapidly evolving discipline, natural language processing (NLP). For more than 60 years, research in this science has been focused on enabling machines to efficiently understand and generate human language. The secrets behind these technological advances lie in LLMs, whose power lies in their ability to capture complex patterns and learn contextual representations of language. How do these LLMs work? What are the available models and how are they evaluated? This book will help you answer these and many other questions. With a technical but accessible introduction: •You will explore the fascinating world of LLMs, from its foundations to its most powerful applications •You will learn how to build your own simple applications with some of the LLMs Designed to guide you step by step, with six chapters combining theory and practice, along with exercises in Python on the Colab platform, you will master the secrets of LLMs and their application in NLP. From deep neural networks and attention mechanisms, to the most relevant LLMs such as BERT, GPT-4, LLaMA, Palm-2 and Falcon, this book guides you through the most important achievements in NLP. Not only will you learn the benchmarks used to evaluate the capabilities of these models, but you will also gain the skill to create your own NLP applications. It will be of great value to professionals, researchers and students within AI, data science and beyond. |
| large language models 101: Generative AI Martin Musiol, 2023-01-08 An engaging and essential discussion of generative artificial intelligence In Generative AI: Navigating the Course to the Artificial General Intelligence Future, celebrated author Martin Musiol—founder and CEO of generativeAI.net and GenAI Lead for Europe at Infosys—delivers an incisive and one-of-a-kind discussion of the current capabilities, future potential, and inner workings of generative artificial intelligence. In the book, you'll explore the short but eventful history of generative artificial intelligence, what it's achieved so far, and how it's likely to evolve in the future. You'll also get a peek at how emerging technologies are converging to create exciting new possibilities in the GenAI space. Musiol analyzes complex and foundational topics in generative AI, breaking them down into straightforward and easy-to-understand pieces. You'll also find: Bold predictions about the future emergence of Artificial General Intelligence via the merging of current AI models Fascinating explorations of the ethical implications of AI, its potential downsides, and the possible rewards Insightful commentary on Autonomous AI Agents and how AI assistants will become integral to daily life in professional and private contexts Perfect for anyone interested in the intersection of ethics, technology, business, and society—and for entrepreneurs looking to take advantage of this tech revolution—Generative AI offers an intuitive, comprehensive discussion of this fascinating new technology. |
| large language models 101: Deep Learning for Coders with fastai and PyTorch Jeremy Howard, Sylvain Gugger, 2020-06-29 Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala |
| large language models 101: Application of Large Language Models (LLMs) for Software Vulnerability Detection Omar, Marwan, Zangana, Hewa Majeed, 2024-11-01 Large Language Models (LLMs) are redefining the landscape of cybersecurity, offering innovative methods for detecting software vulnerabilities. By applying advanced AI techniques to identify and predict weaknesses in software code, including zero-day exploits and complex malware, LLMs provide a proactive approach to securing digital environments. This integration of AI and cybersecurity presents new possibilities for enhancing software security measures. Application of Large Language Models (LLMs) for Software Vulnerability Detection offers a comprehensive exploration of this groundbreaking field. These chapters are designed to bridge the gap between AI research and practical application in cybersecurity, in order to provide valuable insights for researchers, AI specialists, software developers, and industry professionals. Through real-world examples and actionable strategies, the publication will drive innovation in vulnerability detection and set new standards for leveraging AI in cybersecurity. |
| large language models 101: Introduction to Generative AI Numa Dhamani, Maggie Engler, 2024-02-27 Generative AI tools like ChatGPT are amazing—but how will their use impact our society? This book introduces the world-transforming technology and the strategies you need to use generative AI safely and effectively. Introduction to Generative AI gives you the hows-and-whys of generative AI in accessible language. In this easy-to-read introduction, you’ll learn: How large language models (LLMs) work How to integrate generative AI into your personal and professional workflows Balancing innovation and responsibility The social, legal, and policy landscape around generative AI Societal impacts of generative AI Where AI is going Anyone who uses ChatGPT for even a few minutes can tell that it’s truly different from other chatbots or question-and-answer tools. Introduction to Generative AI guides you from that first eye-opening interaction to how these powerful tools can transform your personal and professional life. In it, you’ll get no-nonsense guidance on generative AI fundamentals to help you understand what these models are (and aren’t) capable of, and how you can use them to your greatest advantage. Foreword by Sahar Massachi. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Generative AI tools like ChatGPT, Bing, and Bard have permanently transformed the way we work, learn, and communicate. This delightful book shows you exactly how Generative AI works in plain, jargon-free English, along with the insights you’ll need to use it safely and effectively. About the book Introduction to Generative AI guides you through benefits, risks, and limitations of Generative AI technology. You’ll discover how AI models learn and think, explore best practices for creating text and graphics, and consider the impact of AI on society, the economy, and the law. Along the way, you’ll practice strategies for getting accurate responses and even understand how to handle misuse and security threats. What's inside How large language models work Integrate Generative AI into your daily work Balance innovation and responsibility About the reader For anyone interested in Generative AI. No technical experience required. About the author Numa Dhamani is a natural language processing expert working at the intersection of technology and society. Maggie Engler is an engineer and researcher currently working on safety for large language models. The technical editor on this book was Maris Sekar. Table of Contents 1 Large language models: The power of AI Evolution of natural language processing 2 Training large language models 3 Data privacy and safety with LLMs 4 The evolution of created content 5 Misuse and adversarial attacks 6 Accelerating productivity: Machine-augmented work 7 Making social connections with chatbots 8 What’s next for AI and LLMs 9 Broadening the horizon: Exploratory topics in AI |
| large language models 101: Natural Language Processing with Transformers, Revised Edition Lewis Tunstall, Leandro von Werra, Thomas Wolf, 2022-05-26 Since their introduction in 2017, transformers have quickly become the dominant architecture for achieving state-of-the-art results on a variety of natural language processing tasks. If you're a data scientist or coder, this practical book -now revised in full color- shows you how to train and scale these large models using Hugging Face Transformers, a Python-based deep learning library. Transformers have been used to write realistic news stories, improve Google Search queries, and even create chatbots that tell corny jokes. In this guide, authors Lewis Tunstall, Leandro von Werra, and Thomas Wolf, among the creators of Hugging Face Transformers, use a hands-on approach to teach you how transformers work and how to integrate them in your applications. You'll quickly learn a variety of tasks they can help you solve. Build, debug, and optimize transformer models for core NLP tasks, such as text classification, named entity recognition, and question answering Learn how transformers can be used for cross-lingual transfer learning Apply transformers in real-world scenarios where labeled data is scarce Make transformer models efficient for deployment using techniques such as distillation, pruning, and quantization Train transformers from scratch and learn how to scale to multiple GPUs and distributed environments |
| large language models 101: How AI Ate the World Chris Stokel-Walker, 2024-05-09 'An excellent starter for those who want to gain an insight into how AI works and why it's likely to shape our lives.' – The Daily Telegraph Artificial intelligence will shake up our lives as thoroughly as the arrival of the internet. This popular, up-to-date book charts AI’s rise from its Cold War origins to its explosive growth in the 2020s. Tech journalist Chris Stokel-Walker (TikTok Boom and YouTubers) goes into the laboratories of the Silicon Valley innovators making rapid advances in ‘large language models’ of machine learning. He meets the insiders at Google and OpenAI who built Gemini and ChatGPT and reveals the extraordinary plans they have for them. Along the way, he explores AI’s dark side by talking to workers who have lost their jobs to bots and engages with futurologists worried that a man-made super-intelligence could threaten humankind. He answers critical questions about the AI revolution, such as what humanity might be jeopardising and the professions that will win and lose – and whether the existential threat technologists Elon Musk and Sam Altman are warning about is realistic – or a smokescreen to divert attention away from their growing power. How AI Ate the World is a ‘start here’ guide for anyone who wants to know more about the world we have just entered. Reviews 'An excellent starter for those who want to gain an insight into how AI works and why it's likely to shape our lives.' The Daily Telegraph 'How AI Ate the World prodigiously captures the key issues and concerns around artificial intelligence.' Azeem Azhar, Exponential View 'From ancient China to Victorian England, How AI Ate The World is the story of the characters, moments, technologies, and relationships that populate the rich history of artificial intelligence... How AI Ate The World grapples with what the age of automation means for the people living through it.' Harry Law, University of Cambridge 'A witty, engaging book that takes us through AI's bumpy past to help us understand its present, and future, impacts. I highly recommend it to anyone who is impacted by AI tech – which is to say, everyone on the planet.' Sasha Luccioni, Hugging Face 'Easily the most comprehensive book on AI I have read so far, covering all the key issues' Peter Hunt, Business & Tech Correspondent, Evening Standard 'A comprehensive and compelling look at the technology that's transforming our world. It's an essential guide, full of surprises, to the technology you need to know.' Matt Navarra, social media expert 'Whether you are new to AI or have been following the AI hype for years, Chris Stokel-Walker offers an entertaining balance of history, context and insight that has something for everyone. The story of AI’s evolution is a complex one, but Stokel-Walker tackles it in a clear, direct way that will bring you up to speed while helping you grapple with what it all means — for individuals, the workplace, society and the planet.' Sharon Goldman, VentureBeat 'This book is a wild, brilliant ride through centuries of thinking about and decades of developing machines that can learn. As a crash course in how we got to this current point of thrilling chaos, it will take some beating. Whether or not you agree with Stokel-Walker’s solutions or not, How AI Ate The World is essential reading to understand where we are and how we got here' Ciaran Martin, former CEO, UK National Cyber Security Centre Buy the book to discover your future |
| large language models 101: Understanding Machine Understanding Ken Clements, 2024-10-15 This is a comprehensive and thought-provoking exploration of the nature of machine understanding, its evaluation, and its implications. The book proposes a new framework, the Multifaceted Understanding Test Tool (MUTT), for assessing machine understanding across multiple dimensions, from language comprehension and logical reasoning to social intelligence and metacognition. Through a combination of philosophical analysis, technical exposition, and narrative thought experiments, the book delves into the frontiers of machine understanding, raising fundamental questions about the cognitive mechanisms and representations that enable genuine understanding in both human and machine minds. By probing the boundaries of artificial comprehension, the book aims to advance our theoretical grasp on the elusive notion of understanding and inform responsible development and deployment of AI technologies. In an era where Artificial Intelligence systems are becoming integral to our daily lives, a pressing question arises: Do these machines truly understand what they are doing, or are they merely sophisticated pattern matchers? Understanding Machine Understanding delves into this profound inquiry, exploring the depths of machine cognition and the essence of comprehension. Join Ken Clements and Claude 3 Opus on an intellectual journey that challenges conventional benchmarks like the Turing Test and introduces the innovative Multifaceted Understanding Test Tool (MUTT). This groundbreaking framework assesses AI's capabilities across language, reasoning, perception, and social intelligence, aiming to distinguish genuine understanding from mere imitation. Through philosophical analysis, technical exposition, and engaging narratives, this book invites readers to explore the frontiers of AI comprehension. Whether you're an AI researcher, philosopher, or curious observer, Understanding Machine Understanding offers a thought-provoking guide to the future of human-machine collaboration. Discover what it truly means for a machine to understand--and the implications for our shared future. |
| large language models 101: Coding with AI For Dummies Chris Minnick, 2024-02-23 Boost your coding output and accuracy with artificial intelligence tools Coding with AI For Dummies introduces you to the many ways that artificial intelligence can make your life as a coder easier. Even if you’re brand new to using AI, this book will show you around the new tools that can produce, examine, and fix code for you. With AI, you can automate processes like code documentation, debugging, updating, and optimization. The time saved thanks to AI lets you focus on the core development tasks that make you even more valuable. Learn the secrets behind coding assistant platforms and get step-by-step instructions on how to implement them to make coding a smoother process. Thanks to AI and this Dummies guide, you’ll be coding faster and better in no time. Discover all the core coding tasks boosted by artificial intelligence Meet the top AI coding assistance platforms currently on the market Learn how to generate documentation with AI and use AI to keep your code up to date Use predictive tools to help speed up the coding process and eliminate bugs This is a great Dummies guide for new and experienced programmers alike. Get started with AI coding and expand your programming toolkit with Coding with AI For Dummies. |
| large language models 101: Applications of Generative AI Zhihan Lyu, |
| large language models 101: Evil Robots, Killer Computers, and Other Myths Steven Shwartz, 2021-02-09 Are AI robots and computers really going to take over the world? Longtime artificial intelligence (AI) researcher and investor Steve Shwartz has grown frustrated with the fear-inducing hype around AI in popular culture and media. Yes, today’s AI systems are miracles of modern engineering, but no, humans do not have to fear robots seizing control or taking over all our jobs. In this exploration of the fascinating and ever-changing landscape of artificial intelligence, Dr. Shwartz explains how AI works in simple terms. After reading this captivating book, you will understand • the inner workings of today’s amazing AI technologies, including facial recognition, self-driving cars, machine translation, chatbots, deepfakes, and many others; • why today’s artificial intelligence technology cannot evolve into the AI of science fiction lore; • the crucial areas where we will need to adopt new laws and policies in order to counter threats to our safety and personal freedoms resulting from the use of AI. So although we don’t have to worry about evil robots rising to power and turning us into pets—and we probably never will—artificial intelligence is here to stay, and we must learn to separate fact from fiction and embrace how this amazing technology enhances our world. |
| large language models 101: Natural Language Interfaces to Databases Yunyao Li, Dragomir Radev, Davood Rafiei, 2023-11-24 This book presents a comprehensive overview of Natural Language Interfaces to Databases (NLIDBs), an indispensable tool in the ever-expanding realm of data-driven exploration and decision making. After first demonstrating the importance of the field using an interactive ChatGPT session, the book explores the remarkable progress and general challenges faced with real-world deployment of NLIDBs. It goes on to provide readers with a holistic understanding of the intricate anatomy, essential components, and mechanisms underlying NLIDBs and how to build them. Key concepts in representing, querying, and processing structured data as well as approaches for optimizing user queries are established for the reader before their application in NLIDBs is explored. The book discusses text to data through early relevant work on semantic parsing and meaning representation before turning to cutting-edge advancements in how NLIDBs are empowered to comprehend and interpret human languages. Various evaluation methodologies, metrics, datasets and benchmarks that play a pivotal role in assessing the effectiveness of mapping natural language queries to formal queries in a database and the overall performance of a system are explored. The book then covers data to text, where formal representations of structured data are transformed into coherent and contextually relevant human-readable narratives. It closes with an exploration of the challenges and opportunities related to interactivity and its corresponding techniques for each dimension, such as instances of conversational NLIDBs and multi-modal NLIDBs where user input is beyond natural language. This book provides a balanced mixture of theoretical insights, practical knowledge, and real-world applications that will be an invaluable resource for researchers, practitioners, and students eager to explore the fundamental concepts of NLIDBs. |
| large language models 101: Methods and Applications of Autonomous Experimentation Marcus Noack, Daniela Ushizima, 2023-12-14 · Provides a holistic and practical guide to autonomous experimentation · Combines insights from theorists, machine-learning engineers and applied scientists to dispel common myths and misconceptions surrounding autonomous experimentation. · Incorporates practitioners’ first-hand experience |
| large language models 101: Gpt-4 for Developers OSWALD. CAMPESATO, 2023-12-22 This resource is designed to bridge the gap between theoretical understanding and practical application, making it a useful tool for software developers, data scientists, AI researchers, and tech enthusiasts interested in harnessing the power of GPT-4 in Python environments. The book contains an assortment of Python 3.x code samples that were generated by ChatGPT and GPT-4. Chapter 1 provides an overview of ChatGPT and GPT-4, followed by a chapter which contains Python 3.x code samples for solving various programming tasks in Python. Chapter 3 contains code samples for data visualization, and Chapter 4 contains code samples for linear regression. The final chapter covers visualization with Gen AI (Generative AI) and DALL-E. Companion files with source code and figures are available for downloading. FEATURES Offers an all-encompassing view of ChatGPT and GPT-4, from basics to advanced topics, including functionalities, capabilities, and limitations Contains Python 3.x code samples demonstrating the application of GPT-4 in real-world scenarios Provides a forward-looking perspective on Generative AI and its integration with data visualization and DALL-E Includes companion files with source code, data sets, and figures |
| large language models 101: Introduction to Natural Language Processing Jacob Eisenstein, 2019-10-01 A survey of computational methods for understanding, generating, and manipulating human language, which offers a synthesis of classical representations and algorithms with contemporary machine learning techniques. This textbook provides a technical perspective on natural language processing—methods for building computer software that understands, generates, and manipulates human language. It emphasizes contemporary data-driven approaches, focusing on techniques from supervised and unsupervised machine learning. The first section establishes a foundation in machine learning by building a set of tools that will be used throughout the book and applying them to word-based textual analysis. The second section introduces structured representations of language, including sequences, trees, and graphs. The third section explores different approaches to the representation and analysis of linguistic meaning, ranging from formal logic to neural word embeddings. The final section offers chapter-length treatments of three transformative applications of natural language processing: information extraction, machine translation, and text generation. End-of-chapter exercises include both paper-and-pencil analysis and software implementation. The text synthesizes and distills a broad and diverse research literature, linking contemporary machine learning techniques with the field's linguistic and computational foundations. It is suitable for use in advanced undergraduate and graduate-level courses and as a reference for software engineers and data scientists. Readers should have a background in computer programming and college-level mathematics. After mastering the material presented, students will have the technical skill to build and analyze novel natural language processing systems and to understand the latest research in the field. |
| large language models 101: Transforming Education With Generative AI: Prompt Engineering and Synthetic Content Creation Sharma, Ramesh C., Bozkurt, Aras, 2024-02-07 The rise of generative Artificial Intelligence (AI) signifies a momentous stride in the evolution of Large Language Models (LLMs) within the expansive sphere of Natural Language Processing (NLP). This groundbreaking advancement ripples through numerous facets of our existence, with education, AI literacy, and curriculum enhancement emerging as focal points of transformation. Within the pages of Transforming Education With Generative AI: Prompt Engineering and Synthetic Content Creation, readers embark on a journey into the heart of this transformative phenomenon. Generative AI's influence extends deeply into education, touching the lives of educators, administrators, policymakers, and learners alike. Within the pages of this book, we explore the intricate art of prompt engineering, a skill that shapes the quality of AI-generated educational content. As generative AI becomes increasingly accessible, this comprehensive volume empowers its audience, by providing them with the knowledge needed to navigate and harness the potential of this powerful tool. |
| large language models 101: Rough Sets Mengjun Hu, |
| large language models 101: Cognitive Machine Intelligence Inam Ullah Khan, Salma El Hajjami, Mariya Ouaissa, Salwa Belaqziz, Tarandeep Kaur Bhatia, 2024-08-28 Cognitive Machine Intelligence: Applications, Challenges, and Related Technologies offers a compelling exploration of the transformative landscape shaped by the convergence of machine intelligence, artificial intelligence, and cognitive computing. In this book, the authors navigate through the intricate realms of technology, unveiling the profound impact of cognitive machine intelligence on diverse fields such as communication, healthcare, cybersecurity, and smart city development. The chapters present study on robots and drones to the integration of machine learning with wireless communication networks, IoT, quantum computing, and beyond. The book explores the essential role of machine learning in healthcare, security, and manufacturing. With a keen focus on privacy, trust, and the improvement of human lifestyles, this book stands as a comprehensive guide to the novel techniques and applications driving the evolution of cognitive machine intelligence. The vision presented here extends to smart cities, where AI-enabled techniques contribute to optimal decision-making, and future computing systems address end-to-end delay issues with a central focus on Quality-of-Service metrics. Cognitive Machine Intelligence is an indispensable resource for researchers, practitioners, and enthusiasts seeking a deep understanding of the dynamic landscape at the intersection of artificial intelligence and cognitive computing. This book: Covers a comprehensive exploration of cognitive machine intelligence and its intersection with emerging technologies such as federated learning, blockchain, and 6G and beyond. Discusses the integration of machine learning with various technologies such as wireless communication networks, ad-hoc networks, software-defined networks, quantum computing, and big data. Examines the impact of machine learning on various fields such as healthcare, unmanned aerial vehicles, cybersecurity, and neural networks. Provides a detailed discussion on the challenges and solutions to future computer networks like end-to-end delay issues, Quality of Service (QoS) metrics, and security. Emphasizes the need to ensure privacy and trust while implementing the novel techniques of machine intelligence. It is primarily written for senior undergraduate and graduate students, and academic researchers in the fields of electrical engineering, electronics and communication engineering, and computer engineering. |
| large language models 101: Pattern Recognition and Computer Vision Zhouchen Lin, |
| large language models 101: Introduction to Text Analytics Emily Ohman, 2024-11-01 This easy-to-follow book will revolutionise how you approach text mining and data analysis as well as equipping you with the tools, and confidence, to navigate complex qualitative data. It can be challenging to effectively combine theoretical concepts with practical, real-world applications but this accessible guide provides you with a clear step-by-step approach. Written specifically for students and early career researchers this pragmatic manual will: • Contextualise your learning with real-world data and engaging case studies. • Encourage the application of your new skills with reflective questions. • Enhance your ability to be critical, and reflective, when dealing with imperfect data. Supported by practical online resources, this book is the perfect companion for those looking to gain confidence and independence whilst using transferable data skills. |
| large language models 101: The Artificial Intelligence Playbook Meghan Hargrave, Douglas Fisher, Nancy Frey, 2024-02-29 Time Saving AI Tools that Make Learning More Engaging Busy educators need tools that support their planning and provide them with more time with students. While Artificial Intelligence (AI) has emerged as a promising solution, it can only help if we’re willing to learn how to use it in ways that improve upon what we already do well. The Artificial Intelligence Playbook: Time Saving Tools that Make Learning More Engaging is here to empower teachers to explore AI’s potential and discover practical ways to implement it to enhance their planning and instruction. Two chapters and 6 Educator Functions guide teachers step-by-step through how to purposely use AI to: Compose Writing Prompts and Avoid Plagiarism Manage Content Foster Student Engagement Meet Students’ Instructional Needs Assess Student Learning Continue Lifelong Learning Though AI has the potential to reduce workload for educators, it will never replace teachers. Your connection with students is irreplaceable—and greatly impacts their learning. Consider AI a valuable tool that provides you with more time to build and sustain those vital relationships with students and that can assist them in learning at the very same time. |
| large language models 101: Coding with ChatGPT and Other LLMs Dr. Vincent Austin Hall, 2024-11-29 Leverage LLM (large language models) for developing unmatched coding skills, solving complex problems faster, and implementing AI responsibly Key Features Understand the strengths and weaknesses of LLM-powered software for enhancing performance while minimizing potential issues Grasp the ethical considerations, biases, and legal aspects of LLM-generated code for responsible AI usage Boost your coding speed and improve quality with IDE integration Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionKeeping up with the AI revolution and its application in coding can be challenging, but with guidance from AI and ML expert Dr. Vincent Hall—who holds a PhD in machine learning and has extensive experience in licensed software development—this book helps both new and experienced coders to quickly adopt best practices and stay relevant in the field. You’ll learn how to use LLMs such as ChatGPT and Bard to produce efficient, explainable, and shareable code and discover techniques to maximize the potential of LLMs. The book focuses on integrated development environments (IDEs) and provides tips to avoid pitfalls, such as bias and unexplainable code, to accelerate your coding speed. You’ll master advanced coding applications with LLMs, including refactoring, debugging, and optimization, while examining ethical considerations, biases, and legal implications. You’ll also use cutting-edge tools for code generation, architecting, description, and testing to avoid legal hassles while advancing your career. By the end of this book, you’ll be well-prepared for future innovations in AI-driven software development, with the ability to anticipate emerging LLM technologies and generate ideas that shape the future of development.What you will learn Utilize LLMs for advanced coding tasks, such as refactoring and optimization Understand how IDEs and LLM tools help coding productivity Master advanced debugging to resolve complex coding issues Identify and avoid common pitfalls in LLM-generated code Explore advanced strategies for code generation, testing, and description Develop practical skills to advance your coding career with LLMs Who this book is for This book is for experienced coders and new developers aiming to master LLMs, data scientists and machine learning engineers looking for advanced techniques for coding with LLMs, and AI enthusiasts exploring ethical and legal implications. Tech professionals will find practical insights for innovation and career growth in this book, while AI consultants and tech hobbyists will discover new methods for training and personal projects. |
| large language models 101: Design and Development of Emerging Chatbot Technology Darwish, Dina, 2024-04-09 In the field of information retrieval, the challenge lies in the speed and accuracy with which users can access relevant data. With the increasing complexity of digital interactions, the need for a solution that transcends traditional methods becomes evident. Human involvement and manual investigation are not only time-consuming but also prone to errors, hindering the seamless exchange of information in various sectors. Design and Development of Emerging Chatbot Technology emerges as a comprehensive solution to the predicament posed by traditional information retrieval methods. Focusing on the transformative power of chatbots, it delves into the intricacies of their operation, applications, and development. Designed for academic scholars across diverse disciplines, the book serves as a beacon for those seeking a deeper understanding of chatbots and their potential to revolutionize information retrieval in customer service, education, healthcare, e-commerce, and more. |
| large language models 101: Translating, Interpreting, and Decolonizing Chinese Fairy Tales Juwen Zhang, 2024-10-15 Through meticulous textual and contextual analysis of the sixteenth-century Chinese tale The Seven Brothers and its fifteen contemporary variants, Juwen Zhang unveils the ways in which the translation and illustration of folk and fairy tales can perpetuate racist stereotypes. By critically examining the conscious and unconscious ideological biases harbored by translators, adapters, and illustrators, the author calls for a paradigm shift in translation practices grounded in decolonization and anti-racism to ensure respectful and inclusive representation of diverse cultures. Translating, Interpreting, and Decolonizing Chinese Fairy Tales not only offers insights for translators, researchers, and educators seeking to leverage folktales and picture books for effective children's education and entertainment, but also challenges our preconceived notions of translated and adapted folk and fairy tales. |
| large language models 101: Artificial General Intelligence Julian Togelius, 2024-09-24 How to make AI capable of general intelligence, and what such technology would mean for society. Artificial intelligence 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. |
| large language models 101: Learning Deep Learning Magnus Ekman, 2021-07-19 NVIDIA's Full-Color Guide to Deep Learning: All You Need to Get Started and Get Results To enable everyone to be part of this historic revolution requires the democratization of AI knowledge and resources. This book is timely and relevant towards accomplishing these lofty goals. -- From the foreword by Dr. Anima Anandkumar, Bren Professor, Caltech, and Director of ML Research, NVIDIA Ekman uses a learning technique that in our experience has proven pivotal to success—asking the reader to think about using DL techniques in practice. His straightforward approach is refreshing, and he permits the reader to dream, just a bit, about where DL may yet take us. -- From the foreword by Dr. Craig Clawson, Director, NVIDIA Deep Learning Institute Deep learning (DL) is a key component of today's exciting advances in machine learning and artificial intelligence. Learning Deep Learning is a complete guide to DL. Illuminating both the core concepts and the hands-on programming techniques needed to succeed, this book is ideal for developers, data scientists, analysts, and others--including those with no prior machine learning or statistics experience. After introducing the essential building blocks of deep neural networks, such as artificial neurons and fully connected, convolutional, and recurrent layers, Magnus Ekman shows how to use them to build advanced architectures, including the Transformer. He describes how these concepts are used to build modern networks for computer vision and natural language processing (NLP), including Mask R-CNN, GPT, and BERT. And he explains how a natural language translator and a system generating natural language descriptions of images. Throughout, Ekman provides concise, well-annotated code examples using TensorFlow with Keras. Corresponding PyTorch examples are provided online, and the book thereby covers the two dominating Python libraries for DL used in industry and academia. He concludes with an introduction to neural architecture search (NAS), exploring important ethical issues and providing resources for further learning. Explore and master core concepts: perceptrons, gradient-based learning, sigmoid neurons, and back propagation See how DL frameworks make it easier to develop more complicated and useful neural networks Discover how convolutional neural networks (CNNs) revolutionize image classification and analysis Apply recurrent neural networks (RNNs) and long short-term memory (LSTM) to text and other variable-length sequences Master NLP with sequence-to-sequence networks and the Transformer architecture Build applications for natural language translation and image captioning NVIDIA's invention of the GPU sparked the PC gaming market. The company's pioneering work in accelerated computing--a supercharged form of computing at the intersection of computer graphics, high-performance computing, and AI--is reshaping trillion-dollar industries, such as transportation, healthcare, and manufacturing, and fueling the growth of many others. Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details. |
| large language models 101: Engineering Applications of Neural Networks Lazaros Iliadis, |
| large language models 101: Speech & Language Processing Dan Jurafsky, 2000-09 |
| large language models 101: Generative AI and LLMs S. Balasubramaniam, Seifedine Kadry, Aruchamy Prasanth, Rajesh Kumar Dhanaraj, 2024-09-23 Generative artificial intelligence (GAI) and large language models (LLM) are machine learning algorithms that operate in an unsupervised or semi-supervised manner. These algorithms leverage pre-existing content, such as text, photos, audio, video, and code, to generate novel content. The primary objective is to produce authentic and novel material. In addition, there exists an absence of constraints on the quantity of novel material that they are capable of generating. New material can be generated through the utilization of Application Programming Interfaces (APIs) or natural language interfaces, such as the ChatGPT developed by Open AI and Bard developed by Google. The field of generative artificial intelligence (AI) stands out due to its unique characteristic of undergoing development and maturation in a highly transparent manner, with its progress being observed by the public at large. The current era of artificial intelligence is being influenced by the imperative to effectively utilise its capabilities in order to enhance corporate operations. Specifically, the use of large language model (LLM) capabilities, which fall under the category of Generative AI, holds the potential to redefine the limits of innovation and productivity. However, as firms strive to include new technologies, there is a potential for compromising data privacy, long-term competitiveness, and environmental sustainability. This book delves into the exploration of generative artificial intelligence (GAI) and LLM. It examines the historical and evolutionary development of generative AI models, as well as the challenges and issues that have emerged from these models and LLM. This book also discusses the necessity of generative AI-based systems and explores the various training methods that have been developed for generative AI models, including LLM pretraining, LLM fine-tuning, and reinforcement learning from human feedback. Additionally, it explores the potential use cases, applications, and ethical considerations associated with these models. This book concludes by discussing future directions in generative AI and presenting various case studies that highlight the applications of generative AI and LLM. |
| large language models 101: Digital Wellbeing Caitlin Krause, 2024-09-19 “Digital Wellbeing masterfully explores the intersection of technology and human potential. This book is a must-read for those who want to leverage the power of AI to unlock their creativity and imagination and ultimately invent new means of expression that will go way beyond human language, art, and science of today.” —Ray Kurzweil, inventor, futurist, and author of New York Times bestseller The Singularity Is Nearer Elevate your digital wellbeing by embracing wonder, creativity, and technology Digital Wellbeing is a transformative guide that offers leaders, business professionals, educators, and lifelong learners a path to thrive in the digital age. Krause provides innovative strategies to enhance mindfulness, creativity, and wellbeing in a world enriched by technology. This book empowers readers to leverage digital tools to create meaningful, positive connections and improve their overall quality of life. Discover how to integrate principles of wonder, awe, creativity, and imagination with cutting-edge technology to achieve a balanced and fulfilling digital experience. Learn about frameworks for digital wellbeing and how to apply them effectively. Gain insights on using technology to enhance connection and a sense of belonging. Explore the impact of spatial computing, immersive imagination, virtual reality, and AI on wellbeing. Understand social media's impact on societal expectations and personal interactions. Access actionable strategies for leaders, educators, and individuals to excel digitally. Learn how tech usage can be more intentional and less reactionary. Engage with reflection questions and exercises to deepen understanding and application. Combining the structure of a how-to guide with the depth of a reflective workbook, this book offers practical advice and engaging exercises, all delivered in Krause's distinctive voice. Explore the transformative potential of Digital Wellbeing and learn how to excel in a digitally connected world with wonder and imagination. Begin your journey towards a balanced, enriching digital life today. |
| large language models 101: Intelligence-Based Cardiology and Cardiac Surgery Anthony C Chang, Alfonso Limon, Robert Brisk, Francisco Lopez- Jimenez, Louise Y Sun, 2023-09-06 Intelligence-Based Cardiology and Cardiac Surgery: Artificial Intelligence and Human Cognition in Cardiovascular Medicine provides a comprehensive survey of artificial intelligence concepts and methodologies with real-life applications in cardiovascular medicine. Authored by a senior physician-data scientist, the book presents an intellectual and academic interface between the medical and data science domains. The book's content consists of basic concepts of artificial intelligence and human cognition applications in cardiology and cardiac surgery. This portfolio ranges from big data, machine and deep learning, cognitive computing and natural language processing in cardiac disease states such as heart failure, hypertension and pediatric heart care. The book narrows the knowledge and expertise chasm between the data scientists, cardiologists and cardiac surgeons, inspiring clinicians to embrace artificial intelligence methodologies, educate data scientists about the medical ecosystem, and create a transformational paradigm for healthcare and medicine. - Covers a wide range of relevant topics from real-world data, large language models, and supervised machine learning to deep reinforcement and federated learning - Presents artificial intelligence concepts and their applications in many areas in an easy-to-understand format accessible to clinicians and data scientists - Discusses using artificial intelligence and related technologies with cardiology and cardiac surgery in a myriad of venues and situations - Delineates the necessary elements for successfully implementing artificial intelligence in cardiovascular medicine for improved patient outcomes - Presents the regulatory, ethical, legal, and financial issues embedded in artificial intelligence applications in cardiology |
| large language models 101: Big Data Analytics Ulrich Matter, 2023-09-04 - Includes many code examples in R and SQL, with R/SQL scripts freely provided online. - Extensive use of real datasets from empirical economic research and business analytics, with data files freely provided online. - Leads students and practitioners to think critically about where the bottlenecks are in practical data analysis tasks with large data sets, and how to address them. |
| large language models 101: Digitally Curious Andrew Grill, 2024-09-23 A straightforward and accessible explainer of new and upcoming technologies for business leaders In Digitally Curious: Your guide to navigating the future of AI and all things tech, futurist, speaker, and technology trends expert Andrew Grill delivers an easy-to-follow and incisive discussion of current and future technologies, as well as how leading companies are deploying them. The author examines critical business concepts, like the future of work, from a technical and human-centric point of view and how Artificial Intelligence will impact us at work and in society. He includes a broad range of relevant technologies and platforms, offering examples that will be immediately relevant to any industry and business. Digitally Curious offers recent and relevant examples via accessible and revealing interviews with global business leaders from various fields. The book also provides: Actionable insights and end-of-chapter takeaways, with links to further information and additional resources Complimentary access to a companion website created and updated by the author, a 30-year veteran of technology and business Immediately applicable steps you can implement right away to create positive change in your business Digitally Curious is perfect for managers, executives, board members, and other business leaders. It is the ideal resource for anyone looking for a simple and straightforward explanation of how new and upcoming tech and digital trends will impact you at work and in broader society. |
| large language models 101: Security Architecture for Hybrid Cloud Mark Buckwell, Stefaan Van daele, Carsten Horst, 2024-07-25 As the transformation to hybrid multicloud accelerates, businesses require a structured approach to securing their workloads. Adopting zero trust principles demands a systematic set of practices to deliver secure solutions. Regulated businesses, in particular, demand rigor in the architectural process to ensure the effectiveness of security controls and continued protection. This book provides the first comprehensive method for hybrid multicloud security, integrating proven architectural techniques to deliver a comprehensive end-to-end security method with compliance, threat modeling, and zero trust practices. This method ensures repeatability and consistency in the development of secure solution architectures. Architects will learn how to effectively identify threats and implement countermeasures through a combination of techniques, work products, and a demonstrative case study to reinforce learning. You'll examine: The importance of developing a solution architecture that integrates security for clear communication Roles that security architects perform and how the techniques relate to nonsecurity subject matter experts How security solution architecture is related to design thinking, enterprise security architecture, and engineering How architects can integrate security into a solution architecture for applications and infrastructure using a consistent end-to-end set of practices How to apply architectural thinking to the development of new security solutions About the authors Mark Buckwell is a cloud security architect at IBM with 30 years of information security experience. Carsten Horst with more than 20 years of experience in Cybersecurity is a certified security architect and Associate Partner at IBM. Stefaan Van daele has 25 years experience in Cybersecurity and is a Level 3 certified security architect at IBM. |
| large language models 101: Generative AI with Amazon Bedrock Shikhar Kwatra, Bunny Kaushik, 2024-07-31 Become proficient in Amazon Bedrock by taking a hands-on approach to building and scaling generative AI solutions that are robust, secure, and compliant with ethical standards Key Features Learn the foundations of Amazon Bedrock from experienced AWS Machine Learning Specialist Architects Master the core techniques to develop and deploy several AI applications at scale Go beyond writing good prompting techniques and secure scalable frameworks by using advanced tips and tricks Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionThe concept of generative artificial intelligence has garnered widespread interest, with industries looking to leverage it to innovate and solve business problems. Amazon Bedrock, along with LangChain, simplifies the building and scaling of generative AI applications without needing to manage the infrastructure. Generative AI with Amazon Bedrock takes a practical approach to enabling you to accelerate the development and integration of several generative AI use cases in a seamless manner. You’ll explore techniques such as prompt engineering, retrieval augmentation, fine-tuning generative models, and orchestrating tasks using agents. The chapters take you through real-world scenarios and use cases such as text generation and summarization, image and code generation, and the creation of virtual assistants. The latter part of the book shows you how to effectively monitor and ensure security and privacy in Amazon Bedrock. By the end of this book, you’ll have gained a solid understanding of building and scaling generative AI apps using Amazon Bedrock, along with various architecture patterns and security best practices that will help you solve business problems and drive innovation in your organization.What you will learn Explore the generative AI landscape and foundation models in Amazon Bedrock Fine-tune generative models to improve their performance Explore several architecture patterns for different business use cases Gain insights into ethical AI practices, model governance, and risk mitigation strategies Enhance your skills in employing agents to develop intelligence and orchestrate tasks Monitor and understand metrics and Amazon Bedrock model response Explore various industrial use cases and architectures to solve real-world business problems using RAG Stay on top of architectural best practices and industry standards Who this book is for This book is for generalist application engineers, solution engineers and architects, technical managers, ML advocates, data engineers, and data scientists looking to either innovate within their organization or solve business use cases using generative AI. A basic understanding of AWS APIs and core AWS services for machine learning is expected. |
| large language models 101: Uses of Artificial Intelligence in STEM Education Xiaoming Zhai, Joseph Krajcik, 2024-10-24 In the age of rapid technological advancements, the integration of Artificial Intelligence (AI), machine learning (ML), and large language models (LLMs) in Science, Technology, Engineering, and Mathematics (STEM) education has emerged as a transformative force, reshaping pedagogical approaches and assessment methodologies. Uses of AI in STEM Education, comprising 25 chapters, delves deep into the multifaceted realm of AI-driven STEM education. It begins by exploring the challenges and opportunities of AI-based STEM education, emphasizing the intricate balance between human tasks and technological tools. As the chapters unfold, readers learn about innovative AI applications, from automated scoring systems in biology, chemistry, physics, mathematics, and engineering to intelligent tutors and adaptive learning. The book also touches upon the nuances of AI in supporting diverse learners, including students with learning disabilities, and the ethical considerations surrounding AI's growing influence in educational settings. It showcases the transformative potential of AI in reshaping STEM education, emphasizing the need for adaptive pedagogical strategies that cater to diverse learning needs in an AI-centric world. The chapters further delve into the practical applications of AI, from scoring teacher observations and analyzing classroom videos using neural networks to the broader implications of AI for STEM assessment practices. Concluding with reflections on the new paradigm of AI-based STEM education, this book serves as a comprehensive guide for educators, researchers, and policymakers, offering insights into the future of STEM education in an AI-driven world. |
| large language models 101: Enhancing Classroom Dialogue Productiveness Yu Song, 2024-11-08 This book demonstrates how artificial intelligence (AI) can be used to uncover the patterns of classroom dialogue and increase the productiveness of dialogue. In this book, the author uses a range of data mining techniques to explore the productive features and sequential patterns of classroom dialogue. She analyses how the Large Language Model (LLM) as an AI technique can be adapted to enhance dialogue contributions. The book also includes valuable feedback and practical cases from teachers and their dialogue transcripts, facilitating an understanding of AI use and pedagogical development. This book makes original contributions to the field of classroom dialogue and technology, and it will encourage scholars making similar attempts at technological infusion for pedagogical improvement. |
| large language models 101: Automated Data Analytics Soraya Sedkaoui, 2024-10-11 The human mind is endowed with a remarkable capacity for creative synthesis between intuition and reason; this mental alchemy is the source of genius. A new synergy is emerging between human ingenuity and the computational capacity of generative AI models. Automated Data Analytics focuses on this fruitful collaboration between the two to unlock the full potential of data analysis. Together, human ethics and algorithmic productivity have created an alloy stronger than the sum of its parts. The future belongs to this symbiosis between heart and mind, human and machine. If we succeed in harmoniously combining our strengths, it will only be a matter of time before we discover new analytical horizons. This book sets out the foundations of this promising partnership, in which everyone makes their contribution to a common work of considerable scope. History is being forged before our very eyes. It is our responsibility to write it wisely, and to collectively pursue the ideal of augmented intelligence progress. |
| large language models 101: How to Keep Your Research Project on Track Keith Townsend, Mark N. Saunders, 2024-10-03 Bringing together valuable insights from a range of research experts, PhD supervisors and examiners, this thoroughly revised second edition of How to Keep Your Research Project on Track details how to deal with the unexpected difficulties of research, and what to do when a project deviates from the plan. Keith Townsend and Mark N.K. Saunders give us essential insights for carrying out research, as well as developing resilience in academia. |
| large language models 101: Life as No One Knows It Sara Imari Walker, 2024-08-06 An intriguing new scientific theory that explains what life is and how it emerges. What is life? This is among the most difficult open problems in science, right up there with the nature of consciousness and the existence of matter. All the definitions we have fall short. None help us understand how life originates or the full range of possibilities for what life on other planets might look like. In Life as No One Knows It, physicist and astrobiologist Sara Imari Walker argues that solving the origin of life requires radical new thinking and an experimentally testable theory for what life is. This is an urgent issue for efforts to make life from scratch in laboratories here on Earth and missions searching for life on other planets. Walker proposes a new paradigm for understanding what physics encompasses and what we recognize as life. She invites us into a world of maverick scientists working without a map, seeking not just answers but better ways to formulate the biggest questions we have about the universe. The book culminates with the bold proposal of a new theory for identifying and classifying life, one that applies not just to biological life on Earth but to any instance of life in the universe. Rigorous, accessible, and vital, Life as No One Knows It celebrates the mystery of life and the explanatory power of physics. |
Introduction to Large Language Large Models Language Models
Language models •Remember the simple n-gram language model • Assigns probabilities to sequences of words • Generate text by sampling possible next words • Is trained on counts …
Large Language Models - edX
What is a Language Model? Categories: •Generative: find the most likely next word •Classification: find the most likely classification/answer LMs assign probabilities to word …
Large Language Models: the basics - Department of Computer …
What defines a Large Language Model (LLM)? •Size? •Architecture? •Training objectives? •Anything can be called LLM if it’s good for the press release? •Intended Use (my preferred …
Large language models - Bitkom e. V.
What are large language models? AI language models or Large Language Models (LLMs), an execution of so-called »Foundation Models«, are the latest development in the field of Artificial …
Foundation and large language models: fundamentals, challenges ...
Foundation and Large Language Models (FLLMs) are models that are trained using a massive amount of data with the intent to perform a variety of downstream tasks. FLLMs are very …
What’s in the Chatterbox? - Science, Technology and Public Policy …
Large language models (LLMs)—machine learning algorithms that can recognize, summarize, translate, predict, and generate human languages on the basis of very large text-based …
CHAPTER 10 Large Language Models - Stanford University
sight of large language modeling is that many practical NLP tasks can be cast as word prediction, and that a powerful-enough language model can solve them with a high degree of accuracy.
AI 101 – Understanding Generative AI and Large Language Models
A language model is a machine learning model that aims to predict and generate plausible language. Words or characters are converted into numerical tokens. Estimate the probability of …
Understanding Large Language Models
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Teaching Large Language Models to Use Tools at Scale - EECS at …
Large language models (LLMs) have shown impressive advancements in many complex tasks such as mathematical reasoning and program synthesis. Despite this progress, the ability
Large Language Models in Neurology Research and Future Practice
5 Dec 2023 · Large language models (LLMs) have emerged as a powerful tool for analyzing and interpreting enormous amounts of data. Adding to the fervor is the capacity of LLMs as a form …
Understanding the Capabilities, Limitations, and Societal Impact of ...
GPT-3 is one of the largest publicly-disclosed language models | it has 175 billion parameters and was trained on 570 gigabytes of text. For comparison, its predecessor, GPT-2 (which is …
Exploring the landscape of large language models: Foundations ...
In this review paper, we delve into the realm of Large Language Models (LLMs), covering their foundational principles, diverse applications, and nuanced training processes. The article sheds
1 A Survey of Large Language Models - arXiv.org
25 Sep 2024 · from statistical language models to neural language models. Recently, pre-trained language models (PLMs) have been proposed by pre-training Transformer models over large …
MT-Bench-101: A Fine-Grained Benchmark for Evaluating Large …
In this paper, we introduce MT-Bench-101, a new benchmark designed specically for evaluat- ing the chat capabilities of LLMs in multi-turn dialogues, as shown in Figure1.
Using large language models in psychology - Nature
Large language models (LLMs), such as OpenAI’s GPT-4, Google’s Bard or Meta’s LLaMa, have created unprecedented opportunities for analysing and generating language data on a massive...
Eight Things to Know about Large Language Models - arXiv.org
The widespread public deployment of large lan-guage models (LLMs) in recent months has prompted a wave of new attention and engage-ment from advocates, policymakers, and …
Demystifying Data Management for Large Language Models
9 Jun 2024 · In the rapidly evolving landscape of artificial intelligence, Large Language Models (LLMs) like GPT-3 [9] and GPT-4 [114] have become pivotal in advancing our understanding …
Risks and Benefits of Large Language Models for the Environment
Large language models come with risks and opportunities for the environment. Increased use of large language models could afect the environment positively or negatively, with possible …
Introduction to Large Language Large Models Language Models
Language models •Remember the simple n-gram language model • Assigns probabilities to sequences of words • Generate text by sampling possible next words • Is trained on counts computed from lots of text •Large language models are similar and different: • Assigns probabilities to sequences of words
Large Language Models - edX
What is a Language Model? Categories: •Generative: find the most likely next word •Classification: find the most likely classification/answer LMs assign probabilities to word sequences: find the most likely word
Large Language Models: the basics - Department of Computer …
What defines a Large Language Model (LLM)? •Size? •Architecture? •Training objectives? •Anything can be called LLM if it’s good for the press release? •Intended Use (my preferred definition): •LLM are models that have emergent abilities and …
A Comprehensive Overview of Large Language Models - arXiv.org
Large Language Models (LLMs) have recently demonstrated remarkable capabilities in natural language processing tasks and beyond. This success of LLMs has led to a large influx of research contributions in this direction.
Large language models - Bitkom e. V.
What are large language models? AI language models or Large Language Models (LLMs), an execution of so-called »Foundation Models«, are the latest development in the field of Artificial Intelligence – they have captured the public imagination and garnered a great deal of attention.
Foundation and large language models: fundamentals, …
Foundation and Large Language Models (FLLMs) are models that are trained using a massive amount of data with the intent to perform a variety of downstream tasks. FLLMs are very promising drivers for different domains, such as Natural Language Processing (NLP) and other AI-related applications.
What’s in the Chatterbox? - Science, Technology and Public Policy …
Large language models (LLMs)—machine learning algorithms that can recognize, summarize, translate, predict, and generate human languages on the basis of very large text-based datasets—are likely to provide the most convincing computer-generated imitation of human language yet. Because language generated by LLMs will be more
CHAPTER 10 Large Language Models - Stanford University
sight of large language modeling is that many practical NLP tasks can be cast as word prediction, and that a powerful-enough language model can solve them with a high degree of accuracy.
AI 101 – Understanding Generative AI and Large Language Models
A language model is a machine learning model that aims to predict and generate plausible language. Words or characters are converted into numerical tokens. Estimate the probability of a token or sequence of tokens occurring within a longer sequence of tokens.
Understanding Large Language Models
%PDF-1.6 %âãÏÓ 6614 0 obj > endobj 6630 0 obj >/Filter/FlateDecode/ID[986796B929D922B3EC1D8C70B3480E29>05A03E2EA51EF34CA73FEAF09FEE084D>]/Index[6614 179]/Info ...
Teaching Large Language Models to Use Tools at Scale - EECS at …
Large language models (LLMs) have shown impressive advancements in many complex tasks such as mathematical reasoning and program synthesis. Despite this progress, the ability
Large Language Models in Neurology Research and Future …
5 Dec 2023 · Large language models (LLMs) have emerged as a powerful tool for analyzing and interpreting enormous amounts of data. Adding to the fervor is the capacity of LLMs as a form of generative arti cial intelligence (AI) able to construct meaningful and …
Understanding the Capabilities, Limitations, and Societal Impact of ...
GPT-3 is one of the largest publicly-disclosed language models | it has 175 billion parameters and was trained on 570 gigabytes of text. For comparison, its predecessor, GPT-2 (which is functionally similar to GPT-3) has 1.5 billion parameters and was trained on 40 gigabytes of text.
Exploring the landscape of large language models: Foundations ...
In this review paper, we delve into the realm of Large Language Models (LLMs), covering their foundational principles, diverse applications, and nuanced training processes. The article sheds
1 A Survey of Large Language Models - arXiv.org
25 Sep 2024 · from statistical language models to neural language models. Recently, pre-trained language models (PLMs) have been proposed by pre-training Transformer models over large-scale corpora, showing strong capabilities in solving various …
MT-Bench-101: A Fine-Grained Benchmark for Evaluating Large Language ...
In this paper, we introduce MT-Bench-101, a new benchmark designed specically for evaluat- ing the chat capabilities of LLMs in multi-turn dialogues, as shown in Figure1.
Using large language models in psychology - Nature
Large language models (LLMs), such as OpenAI’s GPT-4, Google’s Bard or Meta’s LLaMa, have created unprecedented opportunities for analysing and generating language data on a massive...
Eight Things to Know about Large Language Models - arXiv.org
The widespread public deployment of large lan-guage models (LLMs) in recent months has prompted a wave of new attention and engage-ment from advocates, policymakers, and scholars from many fields. This attention is a timely re-sponse to the many urgent questions that this tech-nology raises, but it can sometimes miss important considerations.
Demystifying Data Management for Large Language Models
9 Jun 2024 · In the rapidly evolving landscape of artificial intelligence, Large Language Models (LLMs) like GPT-3 [9] and GPT-4 [114] have become pivotal in advancing our understanding and capabilities in data intelligence.
Risks and Benefits of Large Language Models for the Environment
Large language models come with risks and opportunities for the environment. Increased use of large language models could afect the environment positively or negatively, with possible direct and indirect efects on the environment and on the way environmental research is conducted.
