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A comprehensive roadmap to using AI in your career and in your life
Artificial intelligence is everywhere. Major software organizations like Microsoft, Google, and Apple have built AI directly into products and invited the world to become part of the AI revolution. And it's impossible to use these tools to their fullest potential without understanding the basics of what AI is and what it can do.
Artificial Intelligence All-in-One For Dummies compiles insight from the expert authors of AI books in the For Dummies series to provide an easy-to-follow walkthrough for anyone interested in learning how to use AI. You'll learn how to put artificial intelligence to work for you and your company in a wide variety of situations, from creating office assistants to managing projects and marketing your products.
Inside the book:
Perfect for professionals curious about the potential and pitfalls associated with generative artificial intelligence, Artificial Intelligence All-in-One For Dummies shows you exactly how AI works and how you can apply it in your own professional and personal life.
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Seitenzahl: 1043
Veröffentlichungsjahr: 2025
Cover
Table of Contents
Title Page
Copyright
Introduction
About This Book
Foolish Assumptions
Icons Used in This Book
Beyond the Book
Where to Go from Here
Book 1: Understanding AI Foundations
Chapter 1: Delving into What AI Means
Defining the Term AI
Understanding the History of AI
Considering AI Uses
Avoiding AI Hype and Overestimation
Connecting AI to the Underlying Computer
Chapter 2: Defining Data’s Role in AI
Finding Data Ubiquitous in This Age
Using Data Successfully
Manicuring the Data
Considering the Five Mistruths in Data
Defining the Limits of Data Acquisition
Considering Data Security Issues
Chapter 3: Considering the Use of Algorithms
Understanding the Role of Algorithms
Discovering the Learning Machine
Chapter 4: Pioneering Specialized Hardware
Relying on Standard Hardware
Using GPUs
Working with Deep Learning Processors (DLPs)
Creating a Specialized Processing Environment
Increasing Hardware Capabilities
Adding Specialized Sensors
Integrating AI with Advanced Sensor Technology
Devising Methods to Interact with the Environment
Chapter 5: Parsing Machine Learning and Deep Learning
Decoding Machine and Deep Learning
Demystifying Natural-Language Processing
Understanding Transformers
Illuminating Generative AI Models
Recognizing AI’s Limitations
Chapter 6: Upholding Responsible AI Standards in GenAI Use
Achieving Originality and Excellence in GenAI-Generated Content
Applying Journalism Ethics to GenAI-Generated Content
Joining the Responsible AI Movement
Chapter 7: Finding Job Security in an AI World
Identifying Tasks That AI Can’t Replace
Upskilling for AI-Proof Jobs
Translating Your Current Skills into AI-Proof Roles
Navigating Career Transitions
Becoming an Early Adopter
Book 2: Prompting and Generative AI Techniques
Chapter 1: Mapping the Lay of the Generative AI Land
So, What Exactly Is Generative AI?
Unveiling the BIG Secret to Working Successfully with GenAI
Understanding the Infamous Finger Problem and Other GenAI Quirks
Figuring Out How to Work with GenAI — It’s All About Your Prompts
Discovering the Differences in GenAI Models and Options
Checking Out Practical Uses of GenAI
Separating Gen AI Fact from Fiction
Chapter 2: Introducing the Art of Prompt Engineering
First Things First: What Is a Prompt?
Revealing the Secret Behind Successful Prompting
Crafting Effective Prompts for Diverse AI Applications
Tips and Tricks for Optimizing Your Prompts
Using Prompts to Provide Supplemental Data for the Model
Avoiding Common Prompting Pitfalls
Chapter 3: Navigating the Evolving Landscape of GenAI
Identifying Key Players and Evaluating GenAI Providers
Getting GenAI that Plays Nice with Other Technologies
Keeping Up with the Pace of GenAI Advancements
Chapter 4: Introducing ChatGPT
Comparing Different Account Versions of ChatGPT
Setting Up an Individual Account
Touring the User Interface
Selecting a GPT Model on the ChatGPT UI
Considering GPT Minis in the GPT Store on the ChatGPT UI
Rendering ChatGPT Outputs to Final Forms
Chapter 5: Getting Started with Microsoft Copilot
Defining Copilot
Signing Up for Copilot
Taking Copilot for a Test Flight
Chapter 6: Learning Advanced Prompting
Starting at the End: Defining Desired Outputs before Prompting
Managing Data for Targeted Impact on Outputs
Adding Data to Prompts
Changing the Model’s Temperature
Changing the Model’s Weights
Book 3: Increasing Productivity with AI
Chapter 1: Applying GenAI in Practical Scenarios
GenAI as Writing Assistant
Getting a Visual Assist from GenAI
Problem-Solving with AI in Creative Projects
Chapter 2: Crunching the Numbers with Copilot
Launching Copilot in Excel
Working with Data
Preparing the Data
Automating Data Analysis
Creating Formulas with Copilot's Assistance
Visualizing Data with Copilot
Considering Copilot's Limitations in Excel
Chapter 3: Presenting with Copilot
Interacting with Copilot in PowerPoint
Designing Slides with Designer
Redesigning Slides with Copilot
Sticking to the Built-in Prompts
Organizing a Presentation
Practicing Your Presentation with Copilot Feedback
Chapter 4: Meeting and Collaborating with Copilot
Using Copilot in Microsoft Teams
Understanding the Limitations of Copilot in Teams
Chapter 5: Working with AI in a Roundup of Business Disciplines
Using GenAI for Marketing
Retrieving Smart Answers for HR
Harnessing GenAI in Legal
Storytelling in Journalism
Consulting GenAI in Healthcare
Cashing In on GenAI in Finance
Using GenAI in IT Operations
Examining New Businesses Based on GenAI
Chapter 6: Managing AI Adoption and Change in Your Organization
Leading AI Adoption and Change Management Efforts
Understanding Different Models for Change and Transition
Overcoming Resistance to AI Adoption
Book 4: Creating Content with AI
Chapter 1: Using AI for Ideation and Planning
Engaging AI to Ideate on Behalf of Human Beings
Deciding Whether AI Hallucinations Are a Feature or a Bug
Following Practical Steps for Idea Generation with AI
Deciding on AI Ideation Tools to Use
Chapter 2: Managing and Writing Emails with AI
Using AI as Your Assistant for Writing Emails
Generating Precise Prompts
Emailing with Copilot
Summarizing with Copilot
Composing Emails with Copilot
Using GenAI for Email with Discernment
Chapter 3: Developing Creative Assets
Trying Out an AI-Generated Where’s Waldo? Illustration
Exploring an Approach for Creating Visual Assets with AI
Enhancing Existing Creative Assets
Fine-Tuning Creativity with AI Tools and Techniques
Choosing AI Tools for Creating Visual Assets
Chapter 4: Producing Long-Form Content
Writing Academic Papers with GenAI Assistance
Developing White Papers and Reports Using Generative AI
Crafting Research Designs and Outlines with GenAI
Integrating Citations and References
Producing Long-Form Articles with GenAI
Writing Books with GenAI
Chapter 5: Search Engine Optimization (SEO) in the AI Era
Describing Search Generative Experiences (SGEs)
Strategies for SEO Success in the AI Era
Enhancing the User Experience with AI
Maximizing Your SEO Efforts
Knowing the AI Tools to Use with SEO
Chapter 6: Fine-Tuning Content with Localization and Translation
Exploiting AI for Localization and Translation
Adopting Core Strategies for Localization
Examining Real-Time Localization and Translation Solutions
Book 5: AI at Home
Chapter 1: Relying on AI to Improve Human Interaction
Developing New Ways to Communicate
Exchanging Ideas
Using Multimedia
Embellishing Human Sensory Perception
Chapter 2: Using AI to Address Medical Needs
Implementing Portable Patient Monitoring
Making Humans More Capable
Addressing Special Needs
Completing Analysis in New Ways
Relying on Telepresence
Devising New Surgical Techniques
Performing Tasks Using Automation
Combining Robots and Medical Professionals
Considering Disruptions That AI Causes for Medical Professionals
Chapter 3: Leveraging AI in Education
Using AI Is Here to Stay
Changing the Structure of Education
Flipping the Teaching Model
Leveraging GenAI to Aid Overworked Educators
Changing How Subjects Are Taught
Supporting Special Education Needs
Delivering Data-Driven Insights for Educators
Banning GenAI Stifles Education
Chapter 4: Using GenAI in the Real World
Dying Keywords
Moving from Information Search to Knowledge Assistants
Living with Misinformation and Manipulation
Narrowing Options
Your Brain on GenAI
Chapter 5: Financial Planning and Other Money Matters
Walking through the Stages of Financial Planning
Getting a Handle on Budgeting
Spotlighting College Expenses
Chapter 6: Retirement and Estate Planning
Digging into Retirement Planning
Thinking about Estate Planning
Book 6: Applying AI in Coding
Chapter 1: How Coding Benefits from AI
Banishing Boring Tasks
Helping with Syntax
Linting with AI
Using AI as a Tutor
Pairing Up with AI
Chapter 2: AI Coding Tools
Navigating GitHub Copilot
Exploring Tabnine
Reviewing Replit
Chapter 3: Coding with Chatbots
Improving Your Prompts
Chatting with Github Copilot
Chatting with ChatGPT
Diving into the OpenAI Platform
Developing a Chatbot with OpenAI
Chapter 4: Progressing from Plan to Prototype
Understanding Project Requirements
Generating Code from an SRS
Blending Manually Written and AI-Generated Code
Tips and Tricks for Code Generation
Book 7: Creating Custom AI Solutions
Chapter 1: Personalizing the Customer Journey by Using AI
Discovering the Customer Journey
Introducing AI Personalization
Benefitting from AI Tools for the Customer Journey
Determining How Customers Feel
Providing What Customers Want
Predicting What Customers Will Do
Delivering Information Customers Need
Automating the Delivery of Content
Chapter 2: Boosting Online Business Growth with AI
Outsmarting Your Competitors
Enhancing Brand Building
Maximizing Conversions
Scaling Paid Advertising ROI
Tracking Key Performance Indicators with AI
Innovating New Offers
Chapter 3: Enhancing Customer Service with Conversational AI Chatbots
Finding Out about Conversational AI Chatbots
Benefitting from Conversational AI Chatbots for Customer Service
Constructing Conversational AI Chatbots
Measuring the Return on Investment of Conversational AI Chatbots
Integrating Conversational AI Chatbots into Existing Systems
Personalizing Customer Interactions
Using Chatbots with Human and AI Collaboration
Considering Best Practices
Reviewing Options for Creating Chatbots
Chapter 4: Making Custom Copilots
Building Your Own Copilot Agent with Copilot Studio
Testing and Editing Your Agent
Publishing Your Agent
Chapter 5: Expanding Copilot’s Capabilities with Plugins
Using Plugins Wisely
Creating a Copilot Plugin
Seeing Examples of Plugins
Considering the Future of Plugins
Index
About the Authors
Connect with Dummies
End User License Agreement
Book 1 Chapter 1
TABLE 1-1 The Kinds of Human Intelligence and How AIs Simulate Them
Book 1 Chapter 5
TABLE 5-1 Token Limits
TABLE 5-2 Parameters in Generative AI Models
Book 1 Chapter 7
TABLE 7-1 Example Skills Transferability Analysis
Book 2 Chapter 1
TABLE 1-1 Common GenAI Myths versus Reality
Book 4 Chapter 1
TABLE 1-1 AI Tools for Idea Generation
Book 4 Chapter 3
TABLE 3-1 AI-Generated Visual Asset Tools
Book 4 Chapter 5
TABLE 5-1 AI Tools That Help with SEO
Book 4 Chapter 6
TABLE 6-1 LLMs for Localization and Translation
TABLE 6-2 Consumer AI Tools for Translation
TABLE 6-3 Enterprise AI Tools for Translation
Book 6 Chapter 2
TABLE 2-1 Copilot Keyboard Shortcuts
Book 7 Chapter 3
TABLE 3-1 Conversational AI Chatbots versus ChatGPT
Book 1 Chapter 1
FIGURE 1-1: An overview of the history of AI.
Book 1 Chapter 2
FIGURE 2-1: With the present AI solutions, more data equates to more intelligen...
Book 1 Chapter 3
FIGURE 3-1: A tree may look like its physical counterpart or have its roots poi...
FIGURE 3-2: Graph nodes can connect to each other in myriad ways.
FIGURE 3-3: A glance at min-max approximation in a tic-tac-toe game.
Book 1 Chapter 5
FIGURE 5-1: The relationship between fields in AI.
FIGURE 5-2: Nodes are arranged in layers.
FIGURE 5-3: A color image containing 3,136 pixels requires a 9,408-neuron input...
FIGURE 5-4: Is it a hot dog?
FIGURE 5-5: Many pictures of hot dogs have similar characteristics.
FIGURE 5-6: Early NLP was based on rules.
FIGURE 5-7: An example of an Alice chatbot.
FIGURE 5-8: Visualizing self-attention.
FIGURE 5-9: Tokenizing a prompt.
FIGURE 5-10: Tokens are represented as token IDs.
FIGURE 5-11: GPT-4 gets an addition problem wrong.
FIGURE 5-12: ChatGPT uses 200 words where 2 will do.
Book 1 Chapter 7
FIGURE 7-1: AI tools can make suggestions about improving written content like ...
FIGURE 7-2: Synthesia lets you create complete videos from text prompts.
Book 2 Chapter 1
FIGURE 1-1: A routine effort to optimize ChatGPT resulted in its producing gibb...
FIGURE 1-2: If data were Legos, GenAI could only build things with the Lego pie...
Book 2 Chapter 2
FIGURE 2-1: Screenshot of ChatGPT user interface.
FIGURE 2-2: Illustration of how prompts work in Azure OpenAI Studio DALL-E play...
FIGURE 2-3: Illustration of how prompts work in Image Generator in OpenAI’s Cha...
Book 2 Chapter 4
FIGURE 4-1: ChatGPT Plus UI.
FIGURE 4-2: A key disclosure in OpenAI’s privacy policy found in full at
https:
...
FIGURE 4-3: Close Sidebar button on the left, chat search icon in the middle, a...
FIGURE 4-4: Upper part of sidebar found on left side of UI showing the New Chat...
FIGURE 4-5: A screenshot of the GPT Store page.
FIGURE 4-6: The list of AI model options offered in the drop-down menu at the t...
FIGURE 4-7: Screenshot of the mid- to lower center of the UI showing the OpenAI...
FIGURE 4-8: A closeup of the prompt bar showing four icons: a paper clip, toolb...
Book 2 Chapter 5
FIGURE 5-1: GitHub Copilot.
FIGURE 5-2: Microsoft Copilot on the web.
FIGURE 5-3: Original Office Assistant, also known as “Clippy.”
FIGURE 5-4: The free version of Copilot Chat.
FIGURE 5-5: You have to log in to use certain Copilot features.
FIGURE 5-6: The Sign In button at
copilot.microsoft.com
.
FIGURE 5-7: The Microsoft account sign in page.
FIGURE 5-8: The Create Account screen.
FIGURE 5-9: Selecting a new email address.
Book 2 Chapter 6
FIGURE 6-1: Customize ChatGPT from the drop-down menu in the upper-right corner...
FIGURE 6-2: You can add files to the prompt bar by selecting the Upload from Co...
FIGURE 6-3: An image of page 134 of
Generative AI for Dummies
.
FIGURE 6-4: Adding the prompt after attaching an image.
FIGURE 6-5: The output that ChatGPT generates after you click the arrow in the ...
FIGURE 6-6: A handwritten note on a napkin can be later entered into ChatGPT wi...
FIGURE 6-7: The prompt asks ChatGPT to plan based on the napkin image from Figu...
FIGURE 6-8: The response from ChatGPT after entering the prompt and uploading t...
FIGURE 6-9: One of nine images that Craiyon offered from my initial prompt.
FIGURE 6-10: An uploaded image along with a prompt that asks ChatGPT to find er...
FIGURE 6-11: ChatGPT responds after being prompted to remember a name.
Book 3 Chapter 1
FIGURE 1-1: A screenshot of a blog post generated in ChatGPT that I’m also usin...
FIGURE 1-2: A screenshot of an image created by the image generator inside Chat...
Book 3 Chapter 2
FIGURE 2-1: The Copilot for Excel sidebar.
FIGURE 2-2: Creating a table from data.
FIGURE 2-3: Asking Copilot in Edge to create a table header.
FIGURE 2-4: Clarifying where the replacement should take place.
FIGURE 2-5: Copilot says there’s an issue with renaming column headers.
FIGURE 2-6: A strange way to sort the year.
FIGURE 2-7: Copilot’s proposed actions.
FIGURE 2-8: Copilot won’t change anything without your approval.
FIGURE 2-9: Copilot’s completion message and Undo button.
FIGURE 2-10: The data tools in Excel.
FIGURE 2-11: Copilot thinks the data is clean.
FIGURE 2-12: Copilot’s insights are often not insightful.
FIGURE 2-13: Copilot’s first insight into the tornado dataset.
FIGURE 2-14: Copilot creates scatter charts but says it can’t.
FIGURE 2-15: A small piece of my weather data spreadsheet.
FIGURE 2-16: Copilot’s proposed formula.
FIGURE 2-17: Copilot’s explanation of its formula.
FIGURE 2-18: Average popularity by year.
FIGURE 2-19: Copilot sometimes just says all the things.
FIGURE 2-20: Max of popularity by year.
FIGURE 2-21: The initial danceability chart created by Copilot.
Book 3 Chapter 3
FIGURE 3-1: The Copilot sidebar in PowerPoint.
FIGURE 3-2: Copilot’s first ideas for my talk.
FIGURE 3-3: A generated slide and a proposed idea.
FIGURE 3-4: A generated PowerPoint presentation.
FIGURE 3-5: Copilot’s section overview page.
FIGURE 3-6: My revised slide is more succinct.
FIGURE 3-7: The Designer button.
FIGURE 3-8: Layout options in the Designer pane.
FIGURE 3-9: Copilot says it can’t change a slide.
FIGURE 3-10: Figuring out what Copilot can and can’t do is sometimes tricky.
FIGURE 3-11: Copilot’s first attempt.
FIGURE 3-12: Copilot’s second attempt.
FIGURE 3-13: Copilot sometimes gets it very wrong.
FIGURE 3-14: Simple, common, and specific images work better.
FIGURE 3-15: Two frogs, but where’s the rest of it?
FIGURE 3-16: The image created by Copilot Chat using DALL-E 3.
FIGURE 3-17: The Rehearse with Coach button.
FIGURE 3-18: The Welcome window.
FIGURE 3-19: Avoid filler words.
FIGURE 3-20: A sample rehearsal report.
Book 3 Chapter 4
FIGURE 4-1: Microsoft 365 Copilot Chat in Teams.
FIGURE 4-2: Allowing transcriptions.
FIGURE 4-3: The Add an Agenda link.
FIGURE 4-4: The Copilot logo in the Agenda window.
FIGURE 4-5: Viewing sample prompts to populate the agenda.
FIGURE 4-6: Copilot’s generated agenda.
FIGURE 4-7: Enabling transcription.
FIGURE 4-8: The Copilot sidebar in Teams.
FIGURE 4-9: Your recap is ready.
FIGURE 4-10: Getting a recap.
Book 3 Chapter 5
FIGURE 5-1: The independent contractor agreement created from a ChatGPT prompt.
FIGURE 5-2: The toolbox under the ChatGPT prompt bar.
FIGURE 5-3: A screenshot of highlighted copy to be edited with the edit bar to ...
FIGURE 5-4: A screenshot of ChatGPT’s suggested edits to contractor’s agreement...
FIGURE 5-5: A list of key elements for artists and entrepreneurs to include in ...
Book 4 Chapter 2
FIGURE 2-1: AI to the rescue.
FIGURE 2-2: The Summary by Copilot link.
FIGURE 2-3: Copilot generates a summary of the thread.
FIGURE 2-4: The Draft with Copilot window.
FIGURE 2-5: Prompting for an email.
FIGURE 2-6: Copilot’s generated email.
FIGURE 2-7: The new draft and the back arrow.
FIGURE 2-8: Copilot’s thank-you poem.
FIGURE 2-9: Copilot provides reply suggestions.
FIGURE 2-10: Starting Coaching by Copilot.
FIGURE 2-11: Coaching doesn’t work on short emails.
FIGURE 2-12: Copilot tells me to work on my enthusiasm.
Book 4 Chapter 4
FIGURE 4-1: Screenshot of the interactive map of global factchecking sites at D...
Book 4 Chapter 5
FIGURE 5-1: Increasing your inflow.
FIGURE 5-2: Decreasing your outflow.
FIGURE 5-3: Alternatives to four-year college degrees.
Book 5 Chapter 6
FIGURE 6-1: One of several answers from AI on reaching an investment goal.
FIGURE 6-2: AI’s explanation of estate planning.
Book 6 Chapter 1
FIGURE 1-1: A ChatGPT-generated HTML template.
FIGURE 1-2: Copilot refused to generate CRUD.
FIGURE 1-3: Adding context to get a better response.
FIGURE 1-4: Running my Node.js application.
FIGURE 1-5: Viewing the collection's contents in MongoDB.
FIGURE 1-6: Code completion is often helpful.
FIGURE 1-7: Copilot’s suggested phone number validation function.
FIGURE 1-8: GenAI models do better when given context.
FIGURE 1-9: Getting syntax support from Copilot.
FIGURE 1-10: GPT-3 doesn’t know about recent additions to JavaScript.
FIGURE 1-11: GPT-4 generates a correct answer when asked about new syntax.
FIGURE 1-12: ChatGPT is usually correct about programming language syntax basic...
FIGURE 1-13: Using Bard as a linter.
FIGURE 1-14: Searching for the Copilot extension.
FIGURE 1-15: Viewing the Copilot menu and multiple suggestion options.
FIGURE 1-16: A somewhat functional trivia game.
Book 6 Chapter 2
FIGURE 2-1: The Copilot status icon in connected and disconnected mode.
FIGURE 2-2: Click the Accounts icon to grant Copilot access to your GitHub acco...
FIGURE 2-3: The Extension Settings screen for Copilot.
FIGURE 2-4: Getting more suggestions.
FIGURE 2-5: Choosing the IDE where you want to install the Tabnine extension.
FIGURE 2-6: Tabnine Hub.
FIGURE 2-7: Viewing previous “magic moments.”
FIGURE 2-8: The Replit home page.
FIGURE 2-9: Coders can advertise and find gigs through Replit Bounties.
FIGURE 2-10: Get started quickly with a template.
FIGURE 2-11: A template’s popularity is often a good indicator of its quality a...
FIGURE 2-12: Viewing more information about a template.
FIGURE 2-13: Your copy of the template in the Replit workspace.
FIGURE 2-14: Viewing the workspace tools panel.
FIGURE 2-15: Rearranging panes.
FIGURE 2-16: The AI tab opens in the right panel.
FIGURE 2-17: Generated HTML and CSS from Replit AI.
FIGURE 2-18: The start of my website for Grapefruit Pulp.
FIGURE 2-19: Replit AI's SVG punk grapefruit.
FIGURE 2-20: Prompting for a JavaScript photo gallery.
FIGURE 2-21: My Replit AI-generated lightbox.
Book 6 Chapter 3
FIGURE 3-1: Asking ChatGPT to rhyme at 0.7 temperature.
FIGURE 3-2: Asking ChatGPT to get more creative.
FIGURE 3-3: Setting the temperature to 2.0 in ChatGPT.
FIGURE 3-4: What it looks like when the model gets too creative.
FIGURE 3-5: Few-shot programming gives examples and specifies the expected form...
FIGURE 3-6: The Copilot Chat plug-in.
FIGURE 3-7: The slash commands in Copilot.
FIGURE 3-8: Copilot correctly identifies the problem with my code and offers to...
FIGURE 3-9: Signing up for a ChatGPT account.
FIGURE 3-10: The ChatGPT UI.
FIGURE 3-11: Opening the Custom Instructions window.
FIGURE 3-12: The Custom Instruction window.
FIGURE 3-13: Viewing ChatGPT’s suggestions for the context custom instructions....
FIGURE 3-14: Example text for the first custom instruction.
FIGURE 3-15: Viewing ChatGPT’s suggestions for the output format custom instruc...
FIGURE 3-16: Custom instructions apply to all new chats.
FIGURE 3-17: The OpenAI Playground.
FIGURE 3-18: Chat mode in the OpenAI Playground.
FIGURE 3-19: The OpenAI Examples page.
FIGURE 3-20: Viewing one of OpenAI’s example prompts.
FIGURE 3-21: Naming your secret key.
FIGURE 3-22: My first attempt at creating a chatbot with ChatGPT was a failure....
FIGURE 3-23: Testing my fixes.
Book 6 Chapter 4
FIGURE 4-1: Asking ChatGPT to help write an SRS.
FIGURE 4-2: My answers to ChatGPT’s questions.
FIGURE 4-3: A ChatGPT-generated SRS.
FIGURE 4-4: Converting to Markdown makes documents more usable.
FIGURE 4-5: The ChatGPT-generated tic-tac-toe game code.
FIGURE 4-6: A frustratingly difficult game of tic-tac-toe.
FIGURE 4-7: An AI tic-tac-toe bot in the OpenAI playground.
FIGURE 4-8: Node.js code to get the next completion from the OpenAI API.
FIGURE 4-9: Suggestions for testing the API server (left) and for handling API ...
FIGURE 4-10: My AI opponent loses track of its instructions.
FIGURE 4-11: Giving more context to the AI.
FIGURE 4-12: The AI doesn’t remember the last command.
FIGURE 4-13: GPT-4 is no good at tic-tac-toe.
Book 7 Chapter 2
FIGURE 2-1: Semrush’s research tools.
FIGURE 2-2: The Ahrefs Keywords Explorer.
FIGURE 2-3: Segment Personas collects your data across different sources you se...
Book 7 Chapter 3
FIGURE 3-1: Manychat has many templates to choose from.
FIGURE 3-2: Chatbotly manages chatbot creation and hosting.
Book 7 Chapter 4
FIGURE 4-1: The Copilot Studio homepage.
FIGURE 4-2: Getting started with the Copilot Studio demo.
FIGURE 4-3: Meet your virtual assistant.
FIGURE 4-4: A response from the
chrisminnick.com
virtual assistant.
FIGURE 4-5: The Copilot Studio welcome slideshow.
FIGURE 4-6: The Copilot Studio homepage.
FIGURE 4-7: Chatting with the agent creator.
FIGURE 4-8: Completing the AI-assisted agent creation process.
FIGURE 4-9: The agent editor.
FIGURE 4-10: The available agent templates.
FIGURE 4-11: The Website Q&A template settings.
FIGURE 4-12: Copilot is sticking to the facts.
FIGURE 4-13: Viewing and editing your agent’s knowledge.
FIGURE 4-14: Viewing available connectors.
FIGURE 4-15: The Topics tab.
FIGURE 4-16: Editing a topic.
FIGURE 4-17: Choosing how to add the topic.
FIGURE 4-18: Describing your topic.
FIGURE 4-19: Editing a new action.
FIGURE 4-20: Opening the conversation map.
FIGURE 4-21: Asking to talk with a person.
FIGURE 4-22: Choose or create an action.
FIGURE 4-23: Configuring the action.
FIGURE 4-24: Viewing inputs and outputs.
FIGURE 4-25: Copilot needs more permission.
FIGURE 4-26: Viewing the result of an action.
FIGURE 4-27: Enabling your copilot to respond to the user.
FIGURE 4-28: Getting the weather forecast.
FIGURE 4-29: The agent's analytics interface.
FIGURE 4-30: The Channels tab.
FIGURE 4-31: How to embed the agent in a website.
FIGURE 4-32: My Wikipedia Reference Bot.
Book 7 Chapter 5
FIGURE 5-1: Asking Copilot for its cutoff date.
FIGURE 5-2: Select your action type.
FIGURE 5-3: The connector’s description.
FIGURE 5-4: Your first action is saved and almost ready to test.
FIGURE 5-5: Your test plugin is now installed.
FIGURE 5-6: The Plugins sidebar.
FIGURE 5-7: The integrated apps settings in Microsoft 365.
FIGURE 5-8: Get Copilot extensions.
FIGURE 5-9: Filtering the apps.
Cover
Table of Contents
Title Page
Copyright
Begin Reading
Index
About the Authors
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Artificial Intelligence All-in-One For Dummies®
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Artificial intelligence (AI) is generally defined as the theory and development of computer systems that can do tasks that normally require human intelligence.
In the early days of what we now call AI, the idea of a mechanical brain existed in the heads of science fiction writers and a few visionaries such as Edmund Callis Berkeley, who wrote “Giant Brains, or Machines that Think” (1949) and Alan Turing, who wrote “Computer Machinery and Intelligence” (1950).
In his famous paper, Turing proposed a test of machine intelligence called the “Imitation Game,” which is known today as the Original Turing Test. The test proposed a party game in which a man and a woman are interrogated by a third party in another room whose goal is to determine which person is the man and which is the woman. Turing then asked whether it would be possible for a digital computer to do well as the interrogator in this game.
In another version of the Turing Test, known today as the “Standard Turing Test,” a human judge evaluates a text transcript of a conversation between a human and a machine and attempts to determine which is which. I recommend you try playing this game at home with ChatGPT or another AI chatbot. Let me know if anyone was fooled by the chatbot. (You can email me at [email protected].)
It wasn’t until the early 2020s that we had AI capable of passing a modern version of the Standard Turing Test. Whether this means that we truly have machines that can think as we do is hotly debated, with most AI experts agreeing that we’re not there (yet) and that the Turing Test is outdated (see Chapter 1 of Book 1).
Theory and history aside, a vast body of technical knowledge about how to create AI systems has developed over the last 100 years. The technical details involve mathematics, computer programming, computer hardware engineering, linguistics, and many other highly technical fields. To attempt to write a book that teaches every aspect of creating AI systems would likely result in a book many times longer than the one you currently hold in your hands (or that’s on your e-reader).
This book is not about the history of AI or about the technical details of creating AI systems. While it does explain the purpose and use of many of the technical topics involved in AI, you don’t need to be a mathematician or computer programmer to read it.
What this book does cover is everything you need to know to make use of the latest AI applications, such as ChatGPT and Microsoft Copilot (among others), to help you in your job, education, home life, finances, creative projects, and much more. Carefully compiled (by yours truly) from the best chapters in 10 different For Dummies books about AI, this single book is your complete guide to working hand in hand (or hand in CPU) with AI, whether your goal is to be more productive, land a new job, start a new AI-focused business, or simply to learn what all the hype is about.
Enjoy, and thank you for reading this book! If you have any questions, please reach out to me at [email protected].
AI is changing the way we work, communicate, research, learn, and just about every other aspect of daily life. How significant the change turns out to be and how much better the current technology can get still remains to be seen. One thing is certain, however: the outputs you get from any AI tool are heavily dependent on the quality of what you put into them. Garbage in, garbage out, as the saying goes.
This book teaches you how to work with AI tools to enhance many of the things you do every day and how to coax AI into giving you consistently high quality responses. In the process, you also learn what AI can (and can’t) do and hear ideas and explanations from an all-star team of technology writers, computer scientists, and AI researchers. I want to point out just one helpful convention that you’ll see throughout the book. When we define a term for you in the text, the term appears in italics.
To make the content more accessible, we divided it into seven mini-books:
Book 1
: Understanding AI Foundations.
In this book, you learn the fundamental concepts of AI, including how algorithms, data, and computers (lots of computers) work together to train machine learning models. You also explore what it means to build and use AI systems responsibly and what the increased use of AI will mean for human workers.
Book 2
: Prompting and Generative AI Techniques.
In this book, you learn about the exciting world of generative AI (GenAI). You gain skills in prompting large language models (LLMs) and learn about two of the most important GenAI applications: OpenAI ChatGPT and Microsoft Copilot.
Book 3
: Increasing Productivity with AI.
In this book, you learn about using GenAI to increase productivity in the workplace. Whether you’re working with text, data, presentations, or other people, GenAI can help you do it more effectively. You also learn about some of the potential pitfalls of AI integrations with common productivity apps and how to manage AI adoption and change in an organization.
Book 4
: Creating Content with AI.
In this book, you learn how to generate content with AI. You see how an AI assistant can help you brainstorm and plan, draft emails, create images, and write long-form content such as blog posts, articles, and even books.
Book 5
: AI at Home.
In this book, you learn about how AI is being used (and will be used in the future) to help people with their everyday lives outside of work. You see the uses and effects that AI is having in a variety of different areas, such as medicine, education, research, and financial planning.
Book 6
: Applying AI in Coding.
In this book, you learn about working with AI assistants to write computer code. If you already know something about writing computer code, AI can help you code faster and better. If you’ve never written a line of code, AI can be your personal tutor and collaborator and help you get from a great idea for your app to a working prototype.
Book 7
: Creating Custom AI Solutions.
If you’re ready to move beyond general-purpose chatbots, this book teaches you how to create custom GenAI agents that can act as customer service representatives, research assistants, and even the secret ingredient that helps you grow your business.
I don’t make many assumptions about you, the reader, but I do have a couple questions for you to ask yourself before you dive into the rest of the book. Answer “Yes” or “No” to each of the following questions to determine whether this book is a good fit for you. Don’t worry; this will be a quick and easy quiz.
Are you curious about how you can use AI to help you in your life, job, recreation, creative projects, and more?
Are you comfortable using a computer, smartphone, or tablet? For example, do you know how to turn it on and access web pages or apps?
Are you willing to keep an open mind while also being appropriately skeptical about the capabilities and limitations of AI?
Those are all the questions I have — I told you it would be easy. If you answered “Yes” to at least one of my questions, you will benefit from the wisdom and experience this book has to offer.
Throughout this book, icons in the margins highlight certain types of valuable information that call out for your attention. Here are the icons you’ll encounter and a brief description of each.
The Tip icon marks tips and shortcuts that you can use to make a task or use a particular tool easier.
Remember icons mark the information that’s especially important to know. To siphon off the most important information in each chapter, just skim through these icons.
The Technical Stuff icon marks information of a highly technical nature that you can normally skip over.
The Warning icon tells you to watch out! It marks important information that may save you headaches or, in the case of AI, will steer you away from questionable, unreliable, or unsafe uses of AI.
In addition to the abundance of information and guidance related to AI that we provide in this book, you get access to even more help and information online at Dummies.com. Check out this book’s online Cheat Sheet. Just go to www.dummies.com and search for “AI All-In-One For Dummies Cheat Sheet.”
Now that you know a little bit about what to expect in the following pages, the next step is up to you! This book isn’t meant to be read straight through cover to cover (although you’re certainly welcome to do that!). If you’re interested in learning a bit about the technical underpinnings of AI, start with Book 1. But these technical details aren’t essential to your being able to use and understand the techniques covered in the other books. If you’d rather skip the technical stuff (for now, at least), find another book or chapter that interests you and dive in!
Book 1
Chapter 1: Delving into What AI Means
Defining the Term AI
Understanding the History of AI
Considering AI Uses
Avoiding AI Hype and Overestimation
Connecting AI to the Underlying Computer
Chapter 2: Defining Data’s Role in AI
Finding Data Ubiquitous in This Age
Using Data Successfully
Manicuring the Data
Considering the Five Mistruths in Data
Defining the Limits of Data Acquisition
Considering Data Security Issues
Chapter 3: Considering the Use of Algorithms
Understanding the Role of Algorithms
Discovering the Learning Machine
Chapter 4: Pioneering Specialized Hardware
Relying on Standard Hardware
Using GPUs
Working with Deep Learning Processors (DLPs)
Creating a Specialized Processing Environment
Increasing Hardware Capabilities
Adding Specialized Sensors
Integrating AI with Advanced Sensor Technology
Devising Methods to Interact with the Environment
Chapter 5: Parsing Machine Learning and Deep Learning
Decoding Machine and Deep Learning
Demystifying Natural-Language Processing
Understanding Transformers
Illuminating Generative AI Models
Recognizing AI’s Limitations
Chapter 6: Upholding Responsible AI Standards in GenAI Use
Achieving Originality and Excellence in GenAI-Generated Content
Applying Journalism Ethics to GenAI-Generated Content
Joining the Responsible AI Movement
Chapter 7: Finding Job Security in an AI World
Identifying Tasks That AI Can’t Replace
Upskilling for AI-Proof Jobs
Translating Your Current Skills into AI-Proof Roles
Navigating Career Transitions
Becoming an Early Adopter
Chapter 1
IN THIS CHAPTER
Defining AI and its history
Using AI for practical tasks
Seeing through AI hype
Connecting AI with computer technology
Common apps, such as Google Assistant, Alexa, and Siri, have many people using artificial intelligence (AI) daily without even thinking about it. Productivity and creative apps such as ChatGPT, Synesthesia, and Gemini help us focus on the content rather than on how to get there. The media floods our entire social environment with so much information and disinformation that many people see AI as a kind of magic (which it most certainly isn’t). So, the best way to start this book is to define what AI is, what it isn’t, and how it connects to computers today.
Before you can use a term in any meaningful way, you must have a definition for it. After all, if nobody agrees on a meaning, the term has none; it’s just a collection of characters. Defining the idiom (a term whose meaning isn’t clear from the meanings of its constituent elements) is especially important with technical terms that have received more than a little press coverage at various times and in various ways.
Saying that AI is an artificial intelligence doesn’t tell you anything meaningful, which is why people have so many discussions and disagreements over this term. Yes, you can argue that what occurs is artificial, not having come from a natural source. However, the intelligence part is, at best, ambiguous. Even if you don’t necessarily agree with the definition of AI as it appears in the sections that follow, this book uses AI according to that definition, and knowing it will help you follow the text more easily.
People define intelligence in many different ways. However, you can say that intelligence involves certain mental activities composed of the following activities:
Learning:
Having the ability to obtain and process new information
Reasoning:
Being able to manipulate information in various ways
Understanding:
Considering the result of information manipulation
Grasping truths:
Determining the validity of the manipulated information
Seeing relationships:
Divining how validated data interacts with other data
Considering meanings:
Applying truths to particular situations in a manner consistent with their relationship
Separating fact from belief:
Determining whether the data is adequately supported by provable sources that can be demonstrated to be consistently valid
The activities list could easily grow quite long, but even this list is relatively prone to interpretation by anyone who accepts it as viable. As the list implies, however, intelligence often follows a process that a computer system can mimic as part of a simulation:
Set a goal (the information to process and the desired output) based on needs or wants.
Assess the value of any known information in support of the goal.
Gather additional information that could support the goal. The emphasis here is on information that
could
support the goal rather than on information you know
will
support the goal.
Manipulate the data such that it achieves a form consistent with existing information.
Define the relationships and truth values between existing and new information.
Determine whether the goal is achieved.
Modify the goal in light of the new data and its effect on the probability of success.
Repeat Steps 2 through 7 as needed until the goal is achieved (found true) or the possibilities for achieving it are exhausted (found false).
Even though you can create algorithms and provide access to data in support of this process within a computer, a computer’s capability to achieve intelligence is severely limited. For example, a computer is incapable of understanding anything because it relies on machine processes to manipulate data using pure math in a strictly mechanical fashion. Likewise, computers can’t easily separate truth from mistruth (as described in Chapter 2 of this book). In fact, no computer can fully implement any of the mental activities in the earlier list that describes intelligence.
As part of deciding what intelligence actually involves, categorizing intelligence is also helpful. Humans don’t use just one type of intelligence; rather, they rely on multiple intelligences to perform tasks. Howard Gardner, a Harvard psychologist, has defined a number of these types of intelligence (for details, see the article “The Theory of Multiple Intelligences” from Project Zero at Harvard University, https://pz.harvard.edu/resources/the-theory-of-multiple-intelligences). Knowing them helps you relate them to the kinds of tasks a computer can simulate as intelligence. (See Table 1-1 for a modified version of these intelligences with additional description.)
TABLE 1-1 The Kinds of Human Intelligence and How AIs Simulate Them
Type
Simulation Potential
Human Tools
Description
Bodily kinesthetic
Moderate to high
Specialized equipment and real-life objects
Body movements, such as those used by a surgeon or a dancer, require precision and body awareness. Robots commonly use this kind of intelligence to perform repetitive tasks, often with higher precision than humans, but sometimes with less grace. It’s essential to differentiate between human augmentation, such as a surgical device that provides a surgeon with enhanced physical ability, and true independent movement. The former is simply a demonstration of mathematical ability in that it depends on the surgeon for input.
Creative
None
Artistic output, new patterns of thought, inventions, new kinds of musical composition
Creativity is the act of developing a new pattern of thought that results in unique output in the form of art, music, or writing. A truly new kind of product is the result of creativity. An AI can simulate existing patterns of thought and even combine them to create what appears to be a unique presentation but is in reality just a mathematically based version of an existing pattern. To create, an AI would need to possess self-awareness, which would require intrapersonal intelligence.
Interpersonal
Low to moderate
Telephone, audioconferencing, videoconferencing, writing, computer conferencing, email
Interacting with others occurs at several levels. The goal of this form of intelligence is to obtain, exchange, give, or manipulate information based on the experiences of others. Computers can answer basic questions because of keyword input, not because they understand the question. The intelligence occurs while obtaining information, locating suitable keywords, and then giving information based on those keywords. Cross-referencing terms in a lookup table and then acting on the instructions provided by the table demonstrates logical intelligence, not interpersonal intelligence.
Intrapersonal
None
Books, creative materials, diaries, privacy, time
Looking inward to understand one’s own interests and then setting goals based on those interests is now a human-only kind of intelligence. As machines, computers have no desires, interests, wants, or creative abilities. An AI processes numeric input using a set of algorithms and provides an output; it isn’t aware of anything it does, nor does it understand anything it does.
Linguistic (often divided into oral, aural, and written)
Low
Games, multimedia, books, voice recorders, spoken words
Working with words is an essential tool for communication because spoken and written information exchange is far faster than any other form. This form of intelligence includes understanding oral, aural, and written input, managing the input to develop an answer, and providing an understandable answer as output. Discerning just how capable computers are in this form of intelligence is difficult in light of AIs such as ChatGPT because it’s all too easy to create tests in which the AI produces nonsense answers.
Logical mathematical
High (potentially higher than humans)
Logic games, investigations, mysteries, brainteasers
Calculating results, performing comparisons, exploring patterns, and considering relationships are all areas in which computers now excel. When you see a computer defeat a human on a game show, this is the only form of intelligence you’re seeing, out of eight kinds of intelligence. Yes, you may see small bits of other kinds of intelligence, but this is the focus. Basing an assessment of human-versus-computer intelligence on just one area isn’t a good idea.
Naturalist
None
Identification, exploration, discovery, new tool creation
Humans rely on the ability to identify, classify, and manipulate their environment to interact with plants, animals, and other objects. This type of intelligence informs you that one piece of fruit is safe to eat though another is not. It also gives you a desire to learn how things work or to explore the universe and all that is in it.
Visual spatial
Moderate
Models, graphics, charts, photographs, drawings, 3D modeling, video, television, multimedia
Physical-environment intelligence is used by people like sailors and architects (among many others). To move around, humans need to understand their physical environment — that is, its dimensions and characteristics. Every robot or portable computer intelligence requires this capability, but the capability is often difficult to simulate (as with self-driving cars) or less than accurate (as with vacuums that rely as much on bumping as they do on moving intelligently).
As described in the previous section, the first concept that’s important to understand is that AI has little to do with human intelligence. Yes, some AI is modeled to simulate human intelligence, but that’s what it is: a simulation. When thinking about AI, notice an interplay between goal seeking, data processing used to achieve that goal, and data acquisition used to better understand the goal. AI relies on algorithms to achieve a result that may or may not have anything to do with human goals or methods of achieving those goals. With this in mind, you can categorize AI functioning in four ways:
Acting humanly
Thinking humanly
Thinking rationally
Acting rationally
When a computer acts like a human, it best reflects the Turing test, in which the computer succeeds when differentiation between the computer and a human isn’t possible. (For details, see “The Turing Test” at the Alan Turing Internet Scrapbook, www.turing.org.uk/scrapbook/test.html). This category also reflects what most media would have you believe AI is all about. You see it employed for technologies such as natural language processing, knowledge representation, automated reasoning, and machine learning (all four of which must be present to pass the test). To pass the Turing test, an AI should have all four previous technologies and, possibly, integrate other solutions (such as expert systems).
The original Turing test didn’t include any physical contact. Harnad’s Total Turing Test does include physical contact, in the form of perceptual ability interrogation, which means that the computer must also employ both computer vision and robotics to succeed. Here’s a quick overview of other Turing test alternatives:
Reverse Turing test:
A human tries to prove to a computer that the human is not a computer (for example, the Completely Automated Public Turing Test to Tell Computers and Humans Apart, or CAPTCHA).
Minimum intelligent signal test:
Only true/false and yes/no questions appear in the test.
Marcus test:
A computer program simulates watching a television show, and the program is tested with meaningful questions about the show's content.
Lovelace test 2.0:
A test detects AI by examining its ability to create art.
Winograd schema challenge:
This test asks multiple-choice questions in a specific format.