Introduction to Foundation Models
暫譯: 基礎模型導論

Chen, Pin-Yu, Liu, Sijia

Introduction to Foundation Models
  • 出版商: Springer
  • 出版日期: 2025-06-03
  • 售價: $3,020
  • 貴賓價: 9.5 折 $2,869
  • 語言: 英文
  • 頁數: 310
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 3031767691
  • ISBN-13: 9783031767692

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相關主題

商品描述

This book offers an extensive exploration of foundation models, guiding readers through the essential concepts and advanced topics that define this rapidly evolving research area. Designed for those seeking to deepen their understanding and contribute to the development of safer and more trustworthy AI technologies, the book is divided into three parts providing the fundamentals, advanced topics in foundation modes, and safety and trust in foundation models:

  • Part I introduces the core principles of foundation models and generative AI, presents the technical background of neural networks, delves into the learning and generalization of transformers, and finishes with the intricacies of transformers and in-context learning.

  • Part II introduces automated visual prompting techniques, prompting LLMs with privacy, memory-efficient fine-tuning methods, and shows how LLMs can be reprogrammed for time-series machine learning tasks. It explores how LLMs can be reused for speech tasks, how synthetic datasets can be used to benchmark foundation models, and elucidates machine unlearning for foundation models.

  • Part III provides a comprehensive evaluation of the trustworthiness of LLMs, introduces jailbreak attacks and defenses for LLMs, presents safety risks when find-tuning LLMs, introduces watermarking techniques for LLMs, presents robust detection of AI-generated text, elucidates backdoor risks in diffusion models, and presents red-teaming methods for diffusion models.

Mathematical notations are clearly defined and explained throughout, making this book an invaluable resource for both newcomers and seasoned researchers in the field.

商品描述(中文翻譯)

這本書深入探討了基礎模型，指導讀者了解定義這一快速發展研究領域的基本概念和進階主題。該書旨在幫助那些希望加深理解並為開發更安全、更可靠的人工智慧技術做出貢獻的讀者，分為三個部分，提供基礎知識、基礎模型的進階主題，以及基礎模型中的安全性和信任：

- 第一部分介紹了基礎模型和生成式人工智慧的核心原則，呈現神經網絡的技術背景，深入探討變壓器的學習和泛化，並以變壓器的複雜性和上下文學習作結。

- 第二部分介紹了自動化視覺提示技術，針對隱私的提示大型語言模型（LLMs）及記憶效率的微調方法，並展示如何將LLMs重新編程以應對時間序列機器學習任務。它探討了如何重用LLMs於語音任務，如何使用合成數據集來基準測試基礎模型，並闡明了基礎模型的機器遺忘。

- 第三部分全面評估LLMs的可信度，介紹LLMs的越獄攻擊和防禦，呈現微調LLMs時的安全風險，介紹LLMs的水印技術，展示AI生成文本的穩健檢測，闡明擴散模型中的後門風險，並提出擴散模型的紅隊方法。

數學符號在全書中清晰定義和解釋，使這本書成為新手和資深研究人員在該領域中不可或缺的資源。

作者簡介

Dr. Pin-Yu Chen is a principal research scientist at IBM Thomas J. Watson Research Center, Yorktown Heights, NY, USA. He is also the chief scientist of RPI-IBM AI Research Collaboration and PI of ongoing MIT-IBM Watson AI Lab projects. Dr. Chen received his Ph.D. in electrical engineering and computer science from the University of Michigan, Ann Arbor, USA, in 2016. Dr. Chen's recent research focuses on adversarial machine learning of neural networks for robustness and safety. His long-term research vision is to build trustworthy machine learning systems. He received the IJCAI Computers and Thought Award in 2023. He also received the IEEE GLOBECOM 2010 GOLD Best Paper Award and UAI 2022 Best Paper Runner-Up Award. At IBM Research, he received several research accomplishment awards, including IBM Master Inventor, IBM Corporate Technical Award, and IBM Pat Goldberg Memorial Best Paper. He is a co-author of the book "Adversarial Robustness for Machine Learning". He is currently on the editorial board of Transactions on Machine Learning Research and IEEE Transactions on Signal Processing. He is also an Area Chair of several AI and machine learning conferences, and a Distinguished Lecturer of ACM.

Dr. Sijia Liu is currently an Assistant Professor in the CSE department at Michigan State University and an Affiliated Professor at IBM Research. His primary research interests include trustworthy and scalable machine learning (ML), with a recent focus on machine unlearning. He has been recognized with several prestigious awards, including the NSF CAREER award in 2024, the Best Paper Runner-Up Award at the Conference on Uncertainty in Artificial Intelligence (UAI) in 2022, and the Best Student Paper Award at the 42nd IEEE ICASSP in 2017. He has published over 70 papers in top ML/AI conferences based on his record in CSRanking and co-organized several tutorials and workshops on trustworthy and scalable ML.

作者簡介(中文翻譯)

陳品宇博士是美國IBM托馬斯·J·沃森研究中心的首席研究科學家，位於紐約州約克鎮。他也是RPI-IBM人工智慧研究合作的首席科學家，以及正在進行的MIT-IBM沃森人工智慧實驗室項目的主要研究者。陳博士於2016年在美國密西根大學安娜堡分校獲得電機工程與計算機科學的博士學位。陳博士最近的研究專注於神經網絡的對抗性機器學習，以提高其穩健性和安全性。他的長期研究願景是建立可信賴的機器學習系統。他於2023年獲得IJCAI計算機與思維獎，並曾獲得IEEE GLOBECOM 2010金獎最佳論文獎及UAI 2022最佳論文亞軍。在IBM研究部門，他獲得了多項研究成就獎，包括IBM大師發明家、IBM企業技術獎和IBM帕特·戈德堡紀念最佳論文獎。他是《對抗性穩健性機器學習》一書的共同作者。目前，他是《機器學習研究期刊》和《IEEE信號處理期刊》的編輯委員會成員，並擔任多個人工智慧和機器學習會議的區域主席，以及ACM的特邀講者。

劉思佳博士目前是密西根州立大學計算機科學與工程系的助理教授，並且是IBM研究的附屬教授。他的主要研究興趣包括可信賴且可擴展的機器學習（ML），最近專注於機器遺忘。他獲得了多項著名獎項，包括2024年的NSF CAREER獎、2022年人工智慧不確定性會議（UAI）的最佳論文亞軍獎，以及2017年第42屆IEEE ICASSP的最佳學生論文獎。他根據CSRanking的記錄已在頂尖的機器學習/人工智慧會議上發表了70多篇論文，並共同組織了多個有關可信賴且可擴展的機器學習的教程和研討會。

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