| bloomberggpt a large language model for finance: Build a Large Language Model (From Scratch) Sebastian Raschka, 2024-10-29 Learn how to create, train, and tweak large language models (LLMs) by building one from the ground up! In Build a Large Language Model (from Scratch) bestselling author Sebastian Raschka guides you step by step through creating your own LLM. Each stage is explained with clear text, diagrams, and examples. You’ll go from the initial design and creation, to pretraining on a general corpus, and on to fine-tuning for specific tasks. Build a Large Language Model (from Scratch) teaches you how to: • Plan and code all the parts of an LLM • Prepare a dataset suitable for LLM training • Fine-tune LLMs for text classification and with your own data • Use human feedback to ensure your LLM follows instructions • Load pretrained weights into an LLM Build a Large Language Model (from Scratch) takes you inside the AI black box to tinker with the internal systems that power generative AI. As you work through each key stage of LLM creation, you’ll develop an in-depth understanding of how LLMs work, their limitations, and their customization methods. Your LLM can be developed on an ordinary laptop, and used as your own personal assistant. About the technology Physicist Richard P. Feynman reportedly said, “I don’t understand anything I can’t build.” Based on this same powerful principle, bestselling author Sebastian Raschka guides you step by step as you build a GPT-style LLM that you can run on your laptop. This is an engaging book that covers each stage of the process, from planning and coding to training and fine-tuning. About the book Build a Large Language Model (From Scratch) is a practical and eminently-satisfying hands-on journey into the foundations of generative AI. Without relying on any existing LLM libraries, you’ll code a base model, evolve it into a text classifier, and ultimately create a chatbot that can follow your conversational instructions. And you’ll really understand it because you built it yourself! What's inside • Plan and code an LLM comparable to GPT-2 • Load pretrained weights • Construct a complete training pipeline • Fine-tune your LLM for text classification • Develop LLMs that follow human instructions About the reader Readers need intermediate Python skills and some knowledge of machine learning. The LLM you create will run on any modern laptop and can optionally utilize GPUs. About the author Sebastian Raschka is a Staff Research Engineer at Lightning AI, where he works on LLM research and develops open-source software. The technical editor on this book was David Caswell. Table of Contents 1 Understanding large language models 2 Working with text data 3 Coding attention mechanisms 4 Implementing a GPT model from scratch to generate text 5 Pretraining on unlabeled data 6 Fine-tuning for classification 7 Fine-tuning to follow instructions A Introduction to PyTorch B References and further reading C Exercise solutions D Adding bells and whistles to the training loop E Parameter-efficient fine-tuning with LoRA |
| bloomberggpt a large language model for finance: Introduction to Large Language Models for Business Leaders I. Almeida, 2023-09-02 Responsible AI Strategy Beyond Fear and Hype - 2024 Edition Shortlisted for the 2023 HARVEY CHUTE Book Awards recognizing emerging talent and outstanding works in the genre of Business and Enterprise Non-Fiction. Explore the transformative potential of technologies like GPT-4 and Claude 2. These large language models (LLMs) promise to reshape how businesses operate. Aimed at non-technical business leaders, this guide offers a pragmatic approach to leveraging LLMs for tangible benefits, while ensuring ethical considerations aren't sidelined. LLMs can refine processes in marketing, software development, HR, R&D, customer service, and even legal operations. But it's essential to approach them with a balanced view. In this guide, you'll: - Learn about the rapid advancements of LLMs. - Understand complex concepts in simple terms. - Discover practical business applications. - Get strategies for smooth integration. - Assess potential impacts on your team. - Delve into the ethics of deploying LLMs. With a clear aim to inform rather than influence, this book is your roadmap to adopting LLMs thoughtfully, maximizing benefits, and minimizing risks. Let's move beyond the noise and understand how LLMs can genuinely benefit your business. More Than a Book By purchasing this book, you will also be granted free access to the AI Academy platform. There you can view free course modules, test your knowledge through quizzes, attend webinars, and engage in discussion with other readers. You can also view, for free, the first module of the self-paced course AI Fundamentals for Business Leaders, and enjoy video lessons and webinars. No credit card required. AI Academy by Now Next Later AI We are the most trusted and effective learning platform dedicated to empowering leaders with the knowledge and skills needed to harness the power of AI safely and ethically. |
| bloomberggpt a large language model for finance: MoneyGPT James Rickards, 2024-11-12 From the New York Times bestselling author of The New Great Depression and Currency Wars, a telling prediction for how AI will endanger global economic markets and security In November 2022, OpenAI released GPT-4 in a chatbot form to the public. In just two months, it claimed 100 million users—the fastest app to ever reach this benchmark. Since then, AI has become an all-consuming topic, popping up on the news, in ads, on your messenger apps, and in conversations with friends and family. But as AI becomes ubiquitous and grows at an ever-increasing pace, what does it mean for the financial markets? In MoneyGPT, Wall Street veteran and former advisor to the Department of Defense James Rickards paints a comprehensive picture of the danger AI poses to the global financial order, and the insidious ways in which AI will threaten national security. Rickards shows how, while AI is touted to increase efficiency and lower costs, its global implementation in the financial world will actually cause chaos, as selling begets selling and bank runs happen at lightning speed. AI further benefits malicious actors, Rickards argues, because without human empathy or instinct to intervene, threats like total nuclear war that once felt extreme are now more likely. And throughout all this, we must remain vigilant on the question of whose values will be promoted in the age of AI. As Rickards predicts, these systems will fail when we rely on them the most. MoneyGPT shows that the danger is not that AI will malfunction, but that it will function exactly as intended. The peril is not in the algorithms, but in ourselves. And it’s up to us to intervene with old-fashioned human logic and common sense before it’s too late. |
| bloomberggpt a large language model for finance: Large Language Models Uday Kamath, Kevin Keenan, Garrett Somers, Sarah Sorenson, 2024 Large Language Models (LLMs) have emerged as a cornerstone technology, transforming how we interact with information and redefining the boundaries of artificial intelligence. LLMs offer an unprecedented ability to understand, generate, and interact with human language in an intuitive and insightful manner, leading to transformative applications across domains like content creation, chatbots, search engines, and research tools. While fascinating, the complex workings of LLMs -- their intricate architecture, underlying algorithms, and ethical considerations -- require thorough exploration, creating a need for a comprehensive book on this subject. This book provides an authoritative exploration of the design, training, evolution, and application of LLMs. It begins with an overview of pre-trained language models and Transformer architectures, laying the groundwork for understanding prompt-based learning techniques. Next, it dives into methods for fine-tuning LLMs, integrating reinforcement learning for value alignment, and the convergence of LLMs with computer vision, robotics, and speech processing. The book strongly emphasizes practical applications, detailing real-world use cases such as conversational chatbots, retrieval-augmented generation (RAG), and code generation. These examples are carefully chosen to illustrate the diverse and impactful ways LLMs are being applied in various industries and scenarios. Readers will gain insights into operationalizing and deploying LLMs, from implementing modern tools and libraries to addressing challenges like bias and ethical implications. The book also introduces the cutting-edge realm of multimodal LLMs that can process audio, images, video, and robotic inputs. With hands-on tutorials for applying LLMs to natural language tasks, this thorough guide equips readers with both theoretical knowledge and practical skills for leveraging the full potential of large language models. This comprehensive resource is appropriate for a wide audience: students, researchers and academics in AI or NLP, practicing data scientists, and anyone looking to grasp the essence and intricacies of LLMs. |
| bloomberggpt a large language model for finance: Challenges in Large Language Model Development and AI Ethics Gupta, Brij, 2024-08-15 The development of large language models has resulted in artificial intelligence advancements promising transformations and benefits across various industries and sectors. However, this progress is not without its challenges. The scale and complexity of these models pose significant technical hurdles, including issues related to bias, transparency, and data privacy. As these models integrate into decision-making processes, ethical concerns about their societal impact, such as potential job displacement or harmful stereotype reinforcement, become more urgent. Addressing these challenges requires a collaborative effort from business owners, computer engineers, policymakers, and sociologists. Fostering effective research for solutions to address AI ethical challenges may ensure that large language model developments benefit society in a positive way. Challenges in Large Language Model Development and AI Ethics addresses complex ethical dilemmas and challenges of the development of large language models and artificial intelligence. It analyzes ethical considerations involved in the design and implementation of large language models, while exploring aspects like bias, accountability, privacy, and social impacts. This book covers topics such as law and policy, model architecture, and machine learning, and is a useful resource for computer engineers, sociologists, policymakers, business owners, academicians, researchers, and scientists. |
| bloomberggpt a large language model for finance: Breaking Barriers with Generative Intelligence. Using GI to Improve Human Education and Well-Being Azza Basiouni, |
| bloomberggpt a large language model for finance: Generative AI on AWS Chris Fregly, Antje Barth, Shelbee Eigenbrode, 2023-11-13 Companies today are moving rapidly to integrate generative AI into their products and services. But there's a great deal of hype (and misunderstanding) about the impact and promise of this technology. With this book, Chris Fregly, Antje Barth, and Shelbee Eigenbrode from AWS help CTOs, ML practitioners, application developers, business analysts, data engineers, and data scientists find practical ways to use this exciting new technology. You'll learn the generative AI project life cycle including use case definition, model selection, model fine-tuning, retrieval-augmented generation, reinforcement learning from human feedback, and model quantization, optimization, and deployment. And you'll explore different types of models including large language models (LLMs) and multimodal models such as Stable Diffusion for generating images and Flamingo/IDEFICS for answering questions about images. Apply generative AI to your business use cases Determine which generative AI models are best suited to your task Perform prompt engineering and in-context learning Fine-tune generative AI models on your datasets with low-rank adaptation (LoRA) Align generative AI models to human values with reinforcement learning from human feedback (RLHF) Augment your model with retrieval-augmented generation (RAG) Explore libraries such as LangChain and ReAct to develop agents and actions Build generative AI applications with Amazon Bedrock |
| bloomberggpt a large language model for finance: 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. |
| bloomberggpt a large language model for finance: Proceedings of the NIELIT’s International Conference on Communication, Electronics and Digital Technology Palaiahnakote Shivakumara, |
| bloomberggpt a large language model for finance: Intelligent and Fuzzy Systems Cengiz Kahraman, |
| bloomberggpt a large language model for finance: Real-Time Data Decisions With AI and ChatGPT Techniques Sharma, Priyanka, Jyotiyana, Monika, Kumar, A.V. Senthil, 2024-09-19 Modern businesses face the challenge of how to most effectively harness the power of Artificial Intelligence (AI) to enhance customer engagement and streamline operations. The proliferation of AI tools like ChatGPT offers immense potential. Yet, businesses often need help to navigate the complexities of implementation and maximize the benefits. This gap between AI's promise and its practical application highlights the need for a comprehensive resource that offers practical insights and innovative strategies. Real-Time Data Decisions With AI and ChatGPT Techniques is a groundbreaking book that addresses this critical challenge. By providing a detailed analysis of ChatGPT and other AI tools, this book equips businesses with the knowledge and strategies needed to leverage AI effectively. From algorithmic enhancements to real-world applications, each chapter offers valuable insights and actionable recommendations, making this book an indispensable guide for businesses seeking to capitalize on AI's transformative potential. |
| bloomberggpt a large language model for finance: Large Language Model-Based Solutions Shreyas Subramanian, 2024-04-02 Learn to build cost-effective apps using Large Language Models In Large Language Model-Based Solutions: How to Deliver Value with Cost-Effective Generative AI Applications, Principal Data Scientist at Amazon Web Services, Shreyas Subramanian, delivers a practical guide for developers and data scientists who wish to build and deploy cost-effective large language model (LLM)-based solutions. In the book, you'll find coverage of a wide range of key topics, including how to select a model, pre- and post-processing of data, prompt engineering, and instruction fine tuning. The author sheds light on techniques for optimizing inference, like model quantization and pruning, as well as different and affordable architectures for typical generative AI (GenAI) applications, including search systems, agent assists, and autonomous agents. You'll also find: Effective strategies to address the challenge of the high computational cost associated with LLMs Assistance with the complexities of building and deploying affordable generative AI apps, including tuning and inference techniques Selection criteria for choosing a model, with particular consideration given to compact, nimble, and domain-specific models Perfect for developers and data scientists interested in deploying foundational models, or business leaders planning to scale out their use of GenAI, Large Language Model-Based Solutions will also benefit project leaders and managers, technical support staff, and administrators with an interest or stake in the subject. |
| bloomberggpt a large language model for finance: Next Generation AI Language Models in Research Kashif Naseer Qureshi, Gwanggil Jeon, 2024-11-13 In this comprehensive and cutting-edge volume, Qureshi and Jeon bring together experts from around the world to explore the potential of artificial intelligence models in research and discuss the potential benefits and the concerns and challenges that the rapid development of this field has raised. The international chapter contributor group provides a wealth of technical information on different aspects of AI, including key aspects of AI, deep learning and machine learning models for AI, natural language processing and computer vision, reinforcement learning, ethics and responsibilities, security, practical implementation, and future directions. The contents are balanced in terms of theory, methodologies, and technical aspects, and contributors provide case studies to clearly illustrate the concepts and technical discussions throughout. Readers will gain valuable insights into how AI can revolutionize their work in fields including data analytics and pattern identification, healthcare research, social science research, and more, and improve their technical skills, problem-solving abilities, and evidence-based decision-making. Additionally, they will be cognizant of the limitations and challenges, the ethical implications, and security concerns related to language models, which will enable them to make more informed choices regarding their implementation. This book is an invaluable resource for undergraduate and graduate students who want to understand AI models, recent trends in the area, and technical and ethical aspects of AI. Companies involved in AI development or implementing AI in various fields will also benefit from the book’s discussions on both the technical and ethical aspects of this rapidly growing field. |
| bloomberggpt a large language model for finance: Intelligent Systems Murilo C. Naldi, Reinaldo A. C. Bianchi, 2023-10-11 The three-volume set LNAI 14195, 14196, and 14197 constitutes the refereed proceedings of the 12th Brazilian Conference on Intelligent Systems, BRACIS 2023, which took place in Belo Horizonte, Brazil, in September 2023. The 90 full papers included in the proceedings were carefully reviewed and selected from 242 submissions. They have been organized in topical sections as follows: Part I: Best papers; resource allocation and planning; rules and feature extraction; AI and education; agent systems; explainability; AI models; Part II: Transformer applications; convolutional neural networks; deep learning applications; reinforcement learning and GAN; classification; machine learning analysis; Part III: Evolutionary algorithms; optimization strategies; computer vision; language and models; graph neural networks; pattern recognition; AI applications. |
| bloomberggpt a large language model for finance: Generative AI For Business Leaders I. Almeida, 2023-12-03 2024 Edition. Free access to the AI Academy! One of the books in this collection is shortlisted for the 2023 HARVEY CHUTE Book Awards recognizing emerging talent and outstanding works in the genre of Business and Enterprise Non-Fiction. Byte-sized Learning AI series by Now Next Later AI: Practical guides providing senior decision-makers with a clear, accessible roadmap for harnessing the power of generative AI, enhancing innovation, and boosting business outcomes. Save by buying the entire 3 book series in one single collection and gain free access to the AI Academy platform. There you can view free course modules, test your knowledge through quizzes, attend webinars, and engage in discussion with other readers. Book: Generative AI Transformation Blueprint This practical and concise guide provides senior decision-makers with a clear, accessible roadmap for harnessing the power of generative AI, enhancing innovation, and boosting business outcomes. Drawing on insights from AI-enabled business transformations in diverse sectors, it presents a validated strategic approach. This blueprint not only outlines best practices but also showcases pioneering use cases, integrating them into a cohesive framework for practical implementation. This scenario-based approach helps leaders understand where and how to apply the practices outlined. Spanning across areas from strategic alignment and talent development to ethical governance and sustaining a competitive edge amid relentless underlying progress, it delivers clarity for charting an optimal Generative AI roadmap. Book: Introduction to Large Language Models for Business Leaders: Responsible AI Strategy Beyond Fear and Hype Shortlisted for the 2023 HARVEY CHUTE Explore the transformative potential of technologies like GPT-4 and Claude 2. These large language models (LLMs) promise to reshape how businesses operate. Aimed at non-technical business leaders, this guide offers a pragmatic approach to leveraging LLMs for tangible benefits, while ensuring ethical considerations aren't sidelined. LLMs can refine processes in marketing, software development, HR, R&D, customer service, and even legal operations. But it's essential to approach them with a balanced view. In this guide, you'll: - Learn about the rapid advancements of LLMs. - Understand complex concepts in simple terms. - Discover practical business applications. - Get strategies for smooth integration. - Assess potential impacts on your team. - Delve into the ethics of deploying LLMs. With a clear aim to inform rather than influence, this book is your roadmap to adopting LLMs thoughtfully, maximizing benefits, and minimizing risks. Let's move beyond the noise and understand how LLMs can genuinely benefit your business. Book: Artificial Intelligence Fundamentals for Business Leaders: Up to Date With Generative AI The perfect guide to help non-technical business leaders understand the power of AI: Machine Learning, Neural Networks, and Data Management. Up to date with Generative AI. More Than a Book Collection By purchasing this series, you will also be granted free access to the AI Academy platform. There you can test your knowledge through end-of-chapter quizzes and engage in discussion with other readers. You will also receive free modules and 50% discount toward the enrollment in the self-paced course of the same name and enjoy video summary lessons, instructor-graded assignments, and live sessions. A course certificate will be awarded upon successful completion. AI Academy by Now Next Later AI We are the most trusted and effective learning platform dedicated to empowering leaders with the knowledge and skills needed to harness the power of AI safely and ethically. We are a human-centric organization. Chat with us anytime. |
| bloomberggpt a large language model for finance: Advancing Software Engineering Through AI, Federated Learning, and Large Language Models Sharma, Avinash Kumar, Chanderwal, Nitin, Prajapati, Amarjeet, Singh, Pancham, Kansal, Mrignainy, 2024-05-02 The rapid evolution of software engineering demands innovative approaches to meet the growing complexity and scale of modern software systems. Traditional methods often need help to keep pace with the demands for efficiency, reliability, and scalability. Manual development, testing, and maintenance processes are time-consuming and error-prone, leading to delays and increased costs. Additionally, integrating new technologies, such as AI, ML, Federated Learning, and Large Language Models (LLM), presents unique challenges in terms of implementation and ethical considerations. Advancing Software Engineering Through AI, Federated Learning, and Large Language Models provides a compelling solution by comprehensively exploring how AI, ML, Federated Learning, and LLM intersect with software engineering. By presenting real-world case studies, practical examples, and implementation guidelines, the book ensures that readers can readily apply these concepts in their software engineering projects. Researchers, academicians, practitioners, industrialists, and students will benefit from the interdisciplinary insights provided by experts in AI, ML, software engineering, and ethics. |
| bloomberggpt a large language model for finance: Artificial Intelligence in HCI Helmut Degen, |
| bloomberggpt a large language model for finance: Deep Learning at Scale Suneeta Mall, 2024-06-18 Bringing a deep-learning project into production at scale is quite challenging. To successfully scale your project, a foundational understanding of full stack deep learning, including the knowledge that lies at the intersection of hardware, software, data, and algorithms, is required. This book illustrates complex concepts of full stack deep learning and reinforces them through hands-on exercises to arm you with tools and techniques to scale your project. A scaling effort is only beneficial when it's effective and efficient. To that end, this guide explains the intricate concepts and techniques that will help you scale effectively and efficiently. You'll gain a thorough understanding of: How data flows through the deep-learning network and the role the computation graphs play in building your model How accelerated computing speeds up your training and how best you can utilize the resources at your disposal How to train your model using distributed training paradigms, i.e., data, model, and pipeline parallelism How to leverage PyTorch ecosystems in conjunction with NVIDIA libraries and Triton to scale your model training Debugging, monitoring, and investigating the undesirable bottlenecks that slow down your model training How to expedite the training lifecycle and streamline your feedback loop to iterate model development A set of data tricks and techniques and how to apply them to scale your training model How to select the right tools and techniques for your deep-learning project Options for managing the compute infrastructure when running at scale |
| bloomberggpt a large language model for finance: Data Mining and Big Data Ying Tan, |
| bloomberggpt a large language model for finance: Rules and Reasoning Anna Fensel, Ana Ozaki, Dumitru Roman, Ahmet Soylu, 2023-11-15 This book constitutes the refereed proceedings of the 7th International Joint Conference on Rules and Reasoning, RuleML+RR 2023, held in Oslo, Norway, during September 18–20, 2023. The 13 full papers and 3 short papers included in these proceedings were carefully reviewed and selected from 46 submissions. They focus on all aspects of theoretical advances; novel technologies; innovative applications; knowledge representation; reasoning with rules; and research, development, applications of rule-based systems. |
| bloomberggpt a large language model for finance: Generative AI in Teaching and Learning Hai-Jew, Shalin, 2023-12-05 Generative AI in Teaching and Learning delves into the revolutionary field of generative artificial intelligence and its impact on education. This comprehensive guide explores the multifaceted applications of generative AI in both formal and informal learning environments, shedding light on the ethical considerations and immense opportunities that arise from its implementation. From the early approaches of utilizing generative AI in teaching to its integration into various facets of learning, this book offers a profound analysis of its potential. Teachers, researchers, instructional designers, developers, data analysts, programmers, and learners alike will find valuable insights into harnessing the power of generative AI for educational purposes. |
| bloomberggpt a large language model for finance: Monetary Policy Normalization Paolo Savona, Rainer Stefano Masera, 2023-08-18 In light of the pickup of inflation at the end of 2021 and monetary policy shifts by the world's major central banks, this book examines interrelated issues in the normalization of monetary policy. It covers topics including the role of technological innovations such as derivatives and cryptocurrencies in monetary and financial management, the role of monetary policy in financial crises (especially public debt), and the major repricing needed for central banks and the global economy. In addition, the book discusses the problem of how flexible money should be and the importance of predictive tools for these decisions, with attention to the advances of languages for scientific research, including those on the workings of the economy. The work addresses the geopolitical and social challenges that have arisen as a result of the invasiveness of monetary policy in its various manifestations in the context of major leading currencies. It is aimed at scholars and students of monetary and financial economics. |
| bloomberggpt a large language model for finance: Proceedings of the 12th International Conference on Soft Computing for Problem Solving Millie Pant, |
| bloomberggpt a large language model for finance: Advanced Intelligent Computing Technology and Applications De-Shuang Huang, |
| bloomberggpt a large language model for finance: Intersection of AI and Business Intelligence in Data-Driven Decision-Making Natarajan, Arul Kumar, Galety, Mohammad Gouse, Iwendi, Celestine, Das, Deepthi, Shankar, Achyut, 2024-08-28 In today's rapidly evolving business landscape, organizations are inundated with vast amounts of data, making it increasingly challenging to extract meaningful insights and make informed decisions. The traditional business intelligence (BI) approach must often address the complexity and speed required for effective decision-making in this data-rich environment. As a result, many businesses need help to leverage their data to drive sustainable growth and remain competitive. Intersection of AI and Business Intelligence in Data-Driven Decision-Making presents a transformative solution to this pressing challenge. By exploring the convergence of artificial intelligence (AI) and BI, our book provides a comprehensive framework for leveraging AI-powered BI to revolutionize data analysis, predictive modeling, and decision-making processes. Readers will gain valuable insights into practical applications, emerging trends, and ethical considerations, inspiring and exciting them about the potential of AI in driving business success. |
| bloomberggpt a large language model for finance: Experimental IR Meets Multilinguality, Multimodality, and Interaction Lorraine Goeuriot, |
| bloomberggpt a large language model for finance: Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium and Blue Sky Andrew M. Olney, |
| bloomberggpt a large language model for finance: Beyond AI Ken Huang, Yang Wang, Feng Zhu, Xi Chen, Chunxiao Xing, 2024-01-27 This book explores the transformative potential of ChatGPT, Web3, and their impact on productivity and various industries. It delves into Generative AI (GenAI) and its representative platform ChatGPT, their synergy with Web3, and how they can revolutionize business operations. It covers the potential impact surpassing prior industrial revolutions. After providing an overview of GenAI, ChatGPT, and Web3, it investigates business applications in various industries and areas, such as product management, finance, real estate, gaming, and government, highlighting value creation and operational revolution through their integration. It also explores their impact on content generation, customer service, personalization, and data analysis and examines how the technologies can enhance content quality, customer experiences, sales, revenue, and resource efficiency. Moreover, it addresses security, privacy, and ethics concerns, emphasizing the responsible implementation of ChatGPT and Web3. Written by experts in this field, this book is aimed at business leaders, entrepreneurs, students, investors, and professionals who are seeking insights into ChatGPT, ChatGPT Plug-in, GPT-based autonomous agents, and the integration of Gen AI and Web3 in business applications. |
| bloomberggpt a large language model for finance: Proceedings of the Future Technologies Conference (FTC) 2023, Volume 1 Kohei Arai, 2023-11-01 This book is a collection of thoroughly well-researched studies presented at the Eighth Future Technologies Conference. This annual conference aims to seek submissions from the wide arena of studies like Computing, Communication, Machine Vision, Artificial Intelligence, Ambient Intelligence, Security, and e-Learning. With an impressive 490 paper submissions, FTC emerged as a hybrid event of unparalleled success, where visionary minds explored groundbreaking solutions to the most pressing challenges across diverse fields. These groundbreaking findings open a window for vital conversation on information technologies in our community especially to foster future collaboration with one another. We hope that the readers find this book interesting and inspiring and render their enthusiastic support toward it. |
| bloomberggpt a large language model for finance: Web Information Systems Engineering – WISE 2023 Feng Zhang, Hua Wang, Mahmoud Barhamgi, Lu Chen, Rui Zhou, 2023-10-21 This book constitutes the proceedings of the 24th International Conference on Web Information Systems Engineering, WISE 2023, held in Melbourne, Victoria, Australia, in October 2023. The 33 full and 40 short papers were carefully reviewed and selected from 137 submissions. They were organized in topical sections as follows: text and sentiment analysis; question answering and information retrieval; social media and news analysis; security and privacy; web technologies; graph embeddings and link predictions; predictive analysis and machine learning; recommendation systems; natural language processing (NLP) and databases; data analysis and optimization; anomaly and threat detection; streaming data; miscellaneous; explainability and scalability in AI. |
| bloomberggpt a large language model for finance: The Rise of Machines Adrian David Cheok, Chamari Edirisinghe, Mangesh Lal Shrestha, 2024-11-21 This book provides an in-depth look at the impact of artificial intelligence (AI) on the future of work. The rise of AI and automation is transforming the world of work, and the book explores the implications of this transformation on jobs and skills. It begins by introducing readers to the basics of AI technology and its various applications in the workplace. It then moves on to examine the impact of AI on jobs and skills, including the changing nature of work and the potential for job loss due to automation. It also delves into the ethical implications of AI in the workplace, including the moral and ethical questions that arise when AI is used to make decisions that affect people's lives. Besides exploring the impact of AI on the workforce, the book provides practical advice for preparing for the future of work in the age of AI. This includes the importance of reskilling and upskilling, as well as strategies for adapting to the changing world of work in the age of AI. It concludes with a future outlook, exploring the likely direction of the workforce in the years to come and the importance of preparing for the future with a proactive approach to AI and the workforce. This book provides a comprehensive and accessible look at the impact of AI on the future of work. It is ideal for anyone interested in understanding the implications of AI on the workforce and preparing for the future of work in the age of AI. |
| bloomberggpt a large language model for finance: Computational Intelligence in Engineering and Project Management Pedro Yobanis Piñero Pérez, |
| bloomberggpt a large language model for finance: Artificial Neural Networks and Machine Learning – ICANN 2024 Michael Wand, |
| bloomberggpt a large language model for finance: Apprentice Nation Ryan Craig, 2023-11-07 College isn’t for everyone. It’s time to challenge the status quo and embrace the potential of apprenticeships in tech, healthcare, finance, and more—which can provide a sustainable pathway to economic opportunity. For decades, college has been the only respectable way to access the world of work, despite paralyzing tuition and a dire lack of practical skills that has left 40 percent of college graduates underemployed, unfulfilled, and struggling to repay student loan debt. Education and workforce expert Ryan Craig explores how a modern apprenticeship system will allow students and job seekers to jump-start their careers by learning while they earn—ultimately leading to greater workforce diversity and geographic mobility. With a deep dive into the history behind America’s outdated college system, Craig reveals: The origins of the student debt crises and admissions scandals Why apprenticeships are an effective pathway to career opportunity What America can do to catch up with other nations making apprenticeship opportunities broadly available Where students and job seekers can go to land an apprenticeship Featuring a directory of US apprenticeship programs by industry and location, Apprentice Nation is an accessible blueprint for a country where young Americans of all backgrounds can launch careers in a variety of in-demand fields. With just a few common sense changes to education and workforce development, anapprentice nation will put the American Dream within reach—for everyone. |
| bloomberggpt a large language model for finance: Knowledge Science, Engineering and Management Cungeng Cao, |
| bloomberggpt a large language model for finance: Financial Data Engineering Tamer Khraisha, 2024-10-09 Today, investment in financial technology and digital transformation is reshaping the financial landscape and generating many opportunities. Too often, however, engineers and professionals in financial institutions lack a practical and comprehensive understanding of the concepts, problems, techniques, and technologies necessary to build a modern, reliable, and scalable financial data infrastructure. This is where financial data engineering is needed. A data engineer developing a data infrastructure for a financial product possesses not only technical data engineering skills but also a solid understanding of financial domain-specific challenges, methodologies, data ecosystems, providers, formats, technological constraints, identifiers, entities, standards, regulatory requirements, and governance. This book offers a comprehensive, practical, domain-driven approach to financial data engineering, featuring real-world use cases, industry practices, and hands-on projects. You'll learn: The data engineering landscape in the financial sector Specific problems encountered in financial data engineering The structure, players, and particularities of the financial data domain Approaches to designing financial data identification and entity systems Financial data governance frameworks, concepts, and best practices The financial data engineering lifecycle from ingestion to production The varieties and main characteristics of financial data workflows How to build financial data pipelines using open source tools and APIs Tamer Khraisha, PhD, is a senior data engineer and scientific author with more than a decade of experience in the financial sector. |
| bloomberggpt a large language model for finance: Generative AI Business Applications David E. Sweenor, Yves Mulkers, 2024-01-31 Within the past year, generative AI has broken barriers and transformed how we think about what computers are truly capable of. But, with the marketing hype and generative AI washing of content, it’s increasingly difficult for business leaders and practitioners to go beyond the art of the possible and answer that critical question–how is generative AI actually being used in organizations? With over 70 real-world case studies and applications across 12 different industries and 11 departments, Generative AI Business Applications: An Executive Guide with Real-Life Examples and Case Studies fills a critical knowledge gap for business leaders and practitioners by providing examples of generative AI in action. Diving into the case studies, this TinyTechGuide discusses AI risks, implementation considerations, generative AI operations, AI ethics, and trustworthy AI. The world is transforming before our very eyes. Don’t get left behind—while understanding the powers and perils of generative AI. Full of use cases and real-world applications, this book is designed for business leaders, tech professionals, and IT teams. We provide practical, jargon-free explanations of generative AI's transformative power. Gain a competitive edge in today's marketplace with Generative AI Business Applications: An Executive Guide with Real-Life Examples and Case Studies. Remember, it's not the tech that's tiny, just the book!™ |
| bloomberggpt a large language model for finance: The Deep Learning Architect's Handbook Ee Kin Chin, 2023-12-29 Harness the power of deep learning to drive productivity and efficiency using this practical guide covering techniques and best practices for the entire deep learning life cycle Key Features Interpret your models’ decision-making process, ensuring transparency and trust in your AI-powered solutions Gain hands-on experience in every step of the deep learning life cycle Explore case studies and solutions for deploying DL models while addressing scalability, data drift, and ethical considerations Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionDeep learning enables previously unattainable feats in automation, but extracting real-world business value from it is a daunting task. This book will teach you how to build complex deep learning models and gain intuition for structuring your data to accomplish your deep learning objectives. This deep learning book explores every aspect of the deep learning life cycle, from planning and data preparation to model deployment and governance, using real-world scenarios that will take you through creating, deploying, and managing advanced solutions. You’ll also learn how to work with image, audio, text, and video data using deep learning architectures, as well as optimize and evaluate your deep learning models objectively to address issues such as bias, fairness, adversarial attacks, and model transparency. As you progress, you’ll harness the power of AI platforms to streamline the deep learning life cycle and leverage Python libraries and frameworks such as PyTorch, ONNX, Catalyst, MLFlow, Captum, Nvidia Triton, Prometheus, and Grafana to execute efficient deep learning architectures, optimize model performance, and streamline the deployment processes. You’ll also discover the transformative potential of large language models (LLMs) for a wide array of applications. By the end of this book, you'll have mastered deep learning techniques to unlock its full potential for your endeavors.What you will learn Use neural architecture search (NAS) to automate the design of artificial neural networks (ANNs) Implement recurrent neural networks (RNNs), convolutional neural networks (CNNs), BERT, transformers, and more to build your model Deal with multi-modal data drift in a production environment Evaluate the quality and bias of your models Explore techniques to protect your model from adversarial attacks Get to grips with deploying a model with DataRobot AutoML Who this book is for This book is for deep learning practitioners, data scientists, and machine learning developers who want to explore deep learning architectures to solve complex business problems. Professionals in the broader deep learning and AI space will also benefit from the insights provided, applicable across a variety of business use cases. Working knowledge of Python programming and a basic understanding of deep learning techniques is needed to get started with this book. |
| bloomberggpt a large language model for finance: Mastering AI Jeremy Kahn, 2024-07-09 A Fortune magazine journalist draws on his expertise and extensive contacts among the companies and scientists at the forefront of artificial intelligence to offer dramatic predictions of AI’s impact over the next decade, from reshaping our economy and the way we work, learn, and create to unknitting our social fabric, jeopardizing our democracy, and fundamentally altering the way we think. Within the next five years, Jeremy Kahn predicts, AI will disrupt almost every industry and enterprise, with vastly increased efficiency and productivity. It will restructure the workforce, making AI copilots a must for every knowledge worker. It will revamp education, meaning children around the world can have personal, portable tutors. It will revolutionize health care, making individualized, targeted pharmaceuticals more affordable. It will compel us to reimagine how we make art, compose music, and write and publish books. The potential of generative AI to extend our skills, talents, and creativity as humans is undeniably exciting and promising. But while this new technology has a bright future, it also casts a dark and fearful shadow. AI will provoke pervasive, disruptive, potentially devastating knock-on effects. Leveraging his unrivaled access to the leaders, scientists, futurists, and others who are making AI a reality, Kahn will argue that if not carefully designed and vigilantly regulated AI will deepen income inequality, depressing wages while imposing winner-take-all markets across much of the economy. AI risks undermining democracy, as truth is overtaken by misinformation, racial bias, and harmful stereotypes. Continuing a process begun by the internet, AI will rewire our brains, likely inhibiting our ability to think critically, to remember, and even to get along with one another—unless we all take decisive action to prevent this from happening. Much as Michael Lewis’s classic The New New Thing offered a prescient, insightful, and eminently readable account of life inside the dot-com bubble, Mastering AI delivers much-needed guidance for anyone eager to understand the AI boom—and what comes next. |
| bloomberggpt a large language model for finance: Partial Identification in Econometrics and Related Topics Nguyen Ngoc Thach, |
Introducing BloombergGPT, Bloomberg’s 50-billion parameter …
Mar 30, 2023 · NEW YORK – Bloomberg today released a research paper detailing the development of BloombergGPT TM, a new large-scale generative artificial intelligence (AI) …
BloombergGPT
BloombergGPT is a cutting-edge large language model developed by Bloomberg, consisting of 50 billion parameters. Unlike other models, BloombergGPT is specifically tailored for finance, …
[2303.17564] BloombergGPT: A Large Language Model for …
Mar 30, 2023 · Large Language Models (LLMs) have been shown to be effective on a variety of tasks; however, no LLM specialized for the financial domain has been reported in literature. In …
BloombergGPT: A Large Language Model for Finance
Mar 30, 2023 · BloombergGPT is a 50 billion parameter language model trained on extensive financial and general datasets, outperforming existing models in financial tasks while …
BloombergGPT | Discover AI use cases - GPT-3 Demo
Bloomberg released a research paper detailing the development of BloombergGPTTM, a new large-scale generative artificial intelligence (AI) model. This large language model (LLM) has …
Welcome to BloombergGPT: When LLMs Meet the Finance Sector
Nov 29, 2023 · Large Language Models (LLMs) are proficient in natural language processing and adaptable to new tasks. Bloomberg's BloombergGPT excels in finance-specific tasks. The …
BloombergGPT: A Large Language Model for Finance
Mar 30, 2023 · In this work, we present BloombergGPT, a 50 billion parameter language model that is trained on a wide range of financial data. We construct a 363 billion token dataset …
Welcome to BloombergGPT, a large-scale language model built …
Apr 6, 2023 · Bloomberg has released a research paper detailing the development of BloombergGPT, which has been specifically trained on a wide range of financial data to …
What if ChatGPT was trained on decades of financial news and …
Apr 3, 2023 · Bloomberg today released a research paper detailing the development of BloombergGPT™, a new large-scale generative artificial intelligence (AI) model. This large …
BloombergGPT: Putting Finance to Work using Large Language …
Jun 28, 2023 · BloombergGPT, a language model trained on financial data, represents a groundbreaking development in the application of AI in finance. It aims to enhance …
Introducing BloombergGPT, Bloomberg’s 50-billion parameter …
Mar 30, 2023 · NEW YORK – Bloomberg today released a research paper detailing the development of BloombergGPT TM, a new large-scale generative artificial intelligence (AI) …
BloombergGPT
BloombergGPT is a cutting-edge large language model developed by Bloomberg, consisting of 50 billion parameters. Unlike other models, BloombergGPT is specifically tailored for finance, …
[2303.17564] BloombergGPT: A Large Language Model for …
Mar 30, 2023 · Large Language Models (LLMs) have been shown to be effective on a variety of tasks; however, no LLM specialized for the financial domain has been reported in literature. In …
BloombergGPT: A Large Language Model for Finance
Mar 30, 2023 · BloombergGPT is a 50 billion parameter language model trained on extensive financial and general datasets, outperforming existing models in financial tasks while …
BloombergGPT | Discover AI use cases - GPT-3 Demo
Bloomberg released a research paper detailing the development of BloombergGPTTM, a new large-scale generative artificial intelligence (AI) model. This large language model (LLM) has …
Welcome to BloombergGPT: When LLMs Meet the Finance Sector
Nov 29, 2023 · Large Language Models (LLMs) are proficient in natural language processing and adaptable to new tasks. Bloomberg's BloombergGPT excels in finance-specific tasks. The …
BloombergGPT: A Large Language Model for Finance
Mar 30, 2023 · In this work, we present BloombergGPT, a 50 billion parameter language model that is trained on a wide range of financial data. We construct a 363 billion token dataset …
Welcome to BloombergGPT, a large-scale language model built …
Apr 6, 2023 · Bloomberg has released a research paper detailing the development of BloombergGPT, which has been specifically trained on a wide range of financial data to …
What if ChatGPT was trained on decades of financial news and …
Apr 3, 2023 · Bloomberg today released a research paper detailing the development of BloombergGPT™, a new large-scale generative artificial intelligence (AI) model. This large …
BloombergGPT: Putting Finance to Work using Large Language …
Jun 28, 2023 · BloombergGPT, a language model trained on financial data, represents a groundbreaking development in the application of AI in finance. It aims to enhance …
