ChatGPT and Bard for business automation : achieving AI-driven growth
Description
The launch of ChatGPT represents an epochal event. Its on par with the emergence of the Internet or the launch of the iPhone. But the impact could be even more transformative. ChatGPT crossed over 100 million users in two months. This was the fastest to do so ever. It took TikTok nine months. No doubt, building applications with ChatGPT is quickly become strategic and a must-have. This is why developers must understand this technology and now. And this book will show how. It details how ChatGPT technology works, such as with the transformer and diffusion models. It also illustrates how to use the various APIs, such as GPT-4, ChatGPT, Bard, and Bedrock. This includes understanding embeddings, fine tuning of the models, using zero-shot and few-shot learning, and adjusting the parameters like temperature. This book focuses on the practical uses of this powerful technology. There are step-by-step tutorials on how to create apps for blog generation, sentiment analysis, language translation and integration. This book is an indispensable guide for any developer who wants to be a part of the ChatGPT revolution. What You Will Learn How generative AI models work, their uses cases, benefits and risks Learn about the various generative AI APIs like GPT-4, ChatGPT, Bard, Bedrock and Anthropic Learn about the elements of this technology like prompt engineering, embeddings, fine tuning, and zero-shot/no-shot learning See how to develop various applications, such as to detect sentiment, create blogs and translate languages Who This Book is For Data scientists or data engineers who want to learn how to leverage generative AI APIs, Software engineers or developers who are looking for a new job role in generative AI, Software engineers and developers who want to see how they can integrate generative AI APIs into existing IT environments, Founders or those who work at startups who want to see how to use generative AI.
More Details
Format
Language
English
ISBN
9781484298527, 1484298527
UPC
10.1007/978-1-4842-9852-7
Notes
Description
The launch of ChatGPT represents an epochal event. Its on par with the emergence of the Internet or the launch of the iPhone. But the impact could be even more transformative. ChatGPT crossed over 100 million users in two months. This was the fastest to do so ever. It took TikTok nine months. No doubt, building applications with ChatGPT is quickly become strategic and a must-have. This is why developers must understand this technology and now. And this book will show how. It details how ChatGPT technology works, such as with the transformer and diffusion models. It also illustrates how to use the various APIs, such as GPT-4, ChatGPT, Bard, and Bedrock. This includes understanding embeddings, fine tuning of the models, using zero-shot and few-shot learning, and adjusting the parameters like temperature. This book focuses on the practical uses of this powerful technology. There are step-by-step tutorials on how to create apps for blog generation, sentiment analysis, language translation and integration. This book is an indispensable guide for any developer who wants to be a part of the ChatGPT revolution. What You Will Learn How generative AI models work, their uses cases, benefits and risks Learn about the various generative AI APIs like GPT-4, ChatGPT, Bard, Bedrock and Anthropic Learn about the elements of this technology like prompt engineering, embeddings, fine tuning, and zero-shot/no-shot learning See how to develop various applications, such as to detect sentiment, create blogs and translate languages Who This Book is For Data scientists or data engineers who want to learn how to leverage generative AI APIs, Software engineers or developers who are looking for a new job role in generative AI, Software engineers and developers who want to see how they can integrate generative AI APIs into existing IT environments, Founders or those who work at startups who want to see how to use generative AI.
Local note
O'Reilly O'Reilly Online Learning: Academic/Public Library Edition
Table of Contents
Intro
Contents
About the Author
Chapter 1: New World of Automation
The Impact
Agenda
Conclusion
Chapter 2: ChatGPT
OpenAI
What Is ChatGPT?
Custom Instructions
Plugins
Advanced Data Analysis
Data Analytics
Optical Character Recognition (OCR)
Image Editing
Web Browsing
Usage Policies
ChatGPT Enterprise
Conclusion
Chapter 3: Bard
The Technology
How Bard Works
Tables
Images
Duet AI
Alternatives
Bing Chat
Claude 2
HuggingChat
Inflection AI
Conclusion
Chapter 4: Prompt Engineering
The Challenges with Prompt Engineering
Hire a Prompt Engineer?
Context
Be Specific
Instructions
Zero and Few-Shot Prompting
Parameters and OpenAI Playground
Resources
Conclusion
Chapter 5: Use Cases
Human Resources
Legal
Customer Support
Sales
Marketing
Finance
Project Management
Product Development
AI Code Development Tools
AI Text-to-Image Systems
Conclusion
Chapter 6: Change Management
Definition
Types of Change Management
The Culture
Roles
Change Advisory Board (CAB)
Training
Change Fatigue
The Plan
Implementation
Tools
Boiling the Ocean
Conclusion
Chapter 7: Understanding AI
Brief History
What Is AI?
Machine Learning
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Deep Learning
Flavors of Deep Learning
The Issues with Deep Learning
Generative AI
What Makes Generative AI Different
GANs
VAEs
Diffusion Models
The Transformer
Conclusion
Chapter 8: Data
Types of Data
Understanding Big Data
Database Systems
The Cloud
Data Strategy for AI
Data Collection
Data Evaluation
Data Wrangling
Data Labeling
How Much Data?
Conclusion
Chapter 9: Risks
Bias and Discrimination
Black Box
Physical Safety
Deepfakes
Cybersecurity
Privacy Laws and Regulations
Junk Content
The Cloud
Data Problem
Intellectual Property
Conclusion
Index
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Citations
APA Citation, 7th Edition (style guide)
Taulli, T. (2023). ChatGPT and Bard for business automation: achieving AI-driven growth . Apress L. P..
Chicago / Turabian - Author Date Citation, 17th Edition (style guide)Taulli, Tom. 2023. ChatGPT and Bard for Business Automation: Achieving AI-driven Growth. Berkeley, CA: Apress L. P.
Chicago / Turabian - Humanities (Notes and Bibliography) Citation, 17th Edition (style guide)Taulli, Tom. ChatGPT and Bard for Business Automation: Achieving AI-driven Growth Berkeley, CA: Apress L. P, 2023.
Harvard Citation (style guide)Taulli, T. (2023). Chatgpt and bard for business automation: achieving AI-driven growth. Berkeley, CA: Apress L. P.
MLA Citation, 9th Edition (style guide)Taulli, Tom. ChatGPT and Bard for Business Automation: Achieving AI-driven Growth Apress L. P., 2023.
Note! Citations contain only title, author, edition, publisher, and year published. Citations should be used as a guideline and should be double checked for accuracy. Citation formats are based on standards as of August 2021.
Staff View
Grouped Work ID
99f9e905-0ed0-f67e-6eb1-788d48a62122-eng
Grouping Information
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| Full title | chatgpt and bard for business automation achieving ai driven growth |
| Author | taulli tom |
| Grouping Category | book |
| Last Update | 2025-02-11 03:40:45AM |
| Last Indexed | 2025-05-22 03:29:38AM |
Book Cover Information
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| 505 | 0 | |a Intro -- Contents -- About the Author -- Chapter 1: New World of Automation -- The Impact -- Agenda -- Conclusion -- Chapter 2: ChatGPT -- OpenAI -- What Is ChatGPT? -- Custom Instructions -- Plugins -- Advanced Data Analysis -- Data Analytics -- Optical Character Recognition (OCR) -- Image Editing -- Web Browsing -- Usage Policies -- ChatGPT Enterprise -- Conclusion -- Chapter 3: Bard -- The Technology -- How Bard Works -- Tables -- Images -- Duet AI -- Alternatives -- Bing Chat -- Claude 2 -- HuggingChat -- Inflection AI -- Conclusion -- Chapter 4: Prompt Engineering | |
| 505 | 8 | |a The Challenges with Prompt Engineering -- Hire a Prompt Engineer? -- Context -- Be Specific -- Instructions -- Zero and Few-Shot Prompting -- Parameters and OpenAI Playground -- Resources -- Conclusion -- Chapter 5: Use Cases -- Human Resources -- Legal -- Customer Support -- Sales -- Marketing -- Finance -- Project Management -- Product Development -- AI Code Development Tools -- AI Text-to-Image Systems -- Conclusion -- Chapter 6: Change Management -- Definition -- Types of Change Management -- The Culture -- Roles -- Change Advisory Board (CAB) -- Training -- Change Fatigue -- The Plan | |
| 505 | 8 | |a Implementation -- Tools -- Boiling the Ocean -- Conclusion -- Chapter 7: Understanding AI -- Brief History -- What Is AI? -- Machine Learning -- Supervised Learning -- Unsupervised Learning -- Reinforcement Learning -- Deep Learning -- Flavors of Deep Learning -- The Issues with Deep Learning -- Generative AI -- What Makes Generative AI Different -- GANs -- VAEs -- Diffusion Models -- The Transformer -- Conclusion -- Chapter 8: Data -- Types of Data -- Understanding Big Data -- Database Systems -- The Cloud -- Data Strategy for AI -- Data Collection -- Data Evaluation -- Data Wrangling | |
| 505 | 8 | |a Data Labeling -- How Much Data? -- Conclusion -- Chapter 9: Risks -- Bias and Discrimination -- Black Box -- Physical Safety -- Deepfakes -- Cybersecurity -- Privacy Laws and Regulations -- Junk Content -- The Cloud -- Data Problem -- Intellectual Property -- Conclusion -- Index | |
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