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商品描述
This book delves into the fascinating world of Generative AI, exploring the two key technologies driving its advancements: Large Language Models (LLMs) and Foundation Models (FMs).
Part 1: Foundations
- LLMs Demystified: We begin by understanding LLMs, powerful AI models trained on massive amounts of text data. These models can generate human-quality text, translate languages, write different creative formats, and even answer your questions in an informative way.
- The Rise of FMs: However, LLMs are just a piece of the puzzle. We explore Foundation Models, a broader category encompassing models trained on various data types like images, audio, and even scientific data. These models represent a significant leap forward in AI, offering a more versatile approach to information processing.
Part 2: LLMs and Generative AI Applications
- Training LLMs: We delve into the intricate process of training LLMs, from data acquisition and pre-processing to different training techniques like supervised and unsupervised learning. The chapter also explores challenges like computational resources and data bias, along with best practices for responsible LLM training.
- Fine-Tuning for Specific Tasks: LLMs can be further specialized for targeted tasks through fine-tuning. We explore how fine-tuning allows LLMs to excel in areas like creative writing, code generation, drug discovery, and even music composition.
Part 3: Advanced Topics
- LLM Architectures: We take a deep dive into the technical aspects of LLMs, exploring the workings of Transformer networks, the backbone of modern LLMs. We also examine the role of attention mechanisms in LLM processing and learn about different prominent LLM architectures like GPT-3 and Jurassic-1 Jumbo.
- Scaling Generative AI: Scaling up LLMs presents significant computational challenges. The chapter explores techniques like model parallelism and distributed training to address these hurdles, along with hardware considerations like GPUs and TPUs that facilitate efficient LLM training. Most importantly, we discuss the crucial role of safety and ethics in generative AI development. Mitigating bias, addressing potential risks like deepfakes, and ensuring transparency are all essential for responsible AI development.
Part 4: The Future
- Evolving Generative AI Landscape: We explore emerging trends in LLM research, like the development of even larger and more capable models, along with advancements in explainable AI and the rise of multimodal LLMs that can handle different data types. We also discuss the potential applications of generative AI in unforeseen areas like personalized education and healthcare.
- Societal Impact and the Future of Work: The book concludes by examining the societal and economic implications of generative AI. We explore the potential transformation of industries, the need for workforce reskilling, and the importance of human-AI collaboration. Additionally, the book emphasizes the need for robust regulations to address concerns like bias, data privacy, and transparency in generative AI development.
This book equips you with a comprehensive understanding of generative AI, its core technologies, its applications, and the considerations for its responsible development and deploym
商品描述(中文翻譯)
這本書深入探討了生成式人工智慧的迷人世界，探索推動其進步的兩項關鍵技術：大型語言模型（LLMs）和基礎模型（FMs）。
第一部分：基礎
- LLMs 解密：我們首先了解 LLMs，這些強大的人工智慧模型是在大量文本數據上訓練而成。這些模型能夠生成類似人類的高品質文本、翻譯語言、撰寫各種創意格式，甚至以資訊性方式回答你的問題。
- FMs 的崛起：然而，LLMs 只是整個拼圖的一部分。我們探索基礎模型，這是一個更廣泛的類別，涵蓋了在各種數據類型上訓練的模型，如圖像、音頻，甚至科學數據。這些模型代表了人工智慧的一次重大飛躍，提供了更具多樣性的資訊處理方法。
第二部分：LLMs 和生成式人工智慧應用
- 訓練 LLMs：我們深入探討訓練 LLMs 的複雜過程，從數據獲取和預處理到各種訓練技術，如監督學習和非監督學習。本章還探討了計算資源和數據偏見等挑戰，以及負責任的 LLM 訓練的最佳實踐。
- 針對特定任務的微調：LLMs 可以通過微調進一步專門化以應對特定任務。我們探討微調如何使 LLMs 在創意寫作、代碼生成、藥物發現甚至音樂創作等領域中表現出色。
第三部分：進階主題
- LLM 架構：我們深入研究 LLMs 的技術面，探索 Transformer 網絡的運作，這是現代 LLMs 的基礎。我們還檢視注意力機制在 LLM 處理中的角色，並了解不同的知名 LLM 架構，如 GPT-3 和 Jurassic-1 Jumbo。
- 擴展生成式人工智慧：擴大 LLMs 的規模面臨重大計算挑戰。本章探討了模型並行性和分佈式訓練等技術來應對這些障礙，以及促進高效 LLM 訓練的硬體考量，如 GPU 和 TPU。最重要的是，我們討論了安全性和倫理在生成式人工智慧發展中的關鍵角色。減少偏見、應對潛在風險（如深偽技術）以及確保透明度對於負責任的人工智慧發展至關重要。
第四部分：未來
- 生成式人工智慧的演變：我們探索 LLM 研究中的新興趨勢，如開發更大且更強大的模型，以及可解釋人工智慧的進展和能夠處理不同數據類型的多模態 LLM 的興起。我們還討論生成式人工智慧在個性化教育和醫療等意想不到領域的潛在應用。
- 社會影響與未來工作：本書最後探討了生成式人工智慧的社會和經濟影響。我們探索行業的潛在轉型、勞動力再技能的需求，以及人類與人工智慧合作的重要性。此外，本書強調了針對生成式人工智慧發展中偏見、數據隱私和透明度等問題的強有力規範的必要性。
這本書使你對生成式人工智慧、其核心技術、應用以及負責任的發展和部署考量有全面的理解。
