
Generative AI For Leaders
Description
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Generative AI is our future, and with this action-filled guidebook, you can lift your organization to the next level.
Presented in a language that is accessible and organized, Generative AI for Leaders covers:
- What Generative AI is
- The benefits of Generative AI (including increased productivity and new product development)
- The challenges of Generative AI (including bias, security, and regulation)
- How to develop a Generative AI strategy to help you stand out in the marketplace
- How to build the right team and when to seek outside help
- Best practice methods for training employees on Generative AI
- A deeper look into sequences, word embeddings, and LLMs
- The progress and challenges of detecting Generative AI
- The future of Generative AI
Included is a list of 75+ concrete ideas you can implement today to begin making Generative AI work for your organization.
Best-selling author of The Sentient Machine and CEO of SparkCognition, Amir Husain has covered every nuts-and-bolts detail in this ONLY comprehensive guidebook on Generative AI that you will ever need.
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Content
- Intro
- Introduction
- 1.1 What is Generative AI?
- 1.2 Developing an Intuitive Understanding
- 1.2.1 Is Everything a Sequence?
- 1.3 The Market
- 1.4 Why Should CEOs Care About Generative AI?
- 1.5 Chapter Summary
- 1.6 Key Questions
- The Benefits of Generative AI
- 2.1 Increased Productivity
- 2.2 Improved Decision Making
- 2.2.1 Data Comprehension
- 2.2.2 Predictive Capabilities
- 2.2.3 Creativity in Problem Solving
- 2.3 New Product and Service Development
- 2.3.1 Ideation
- 2.3.2 Design
- 2.3.3 Prototyping
- 2.3.4 Testing
- 2.3.5 Customization
- 2.3.6 Programming with LLMs
- 2.3.7 Final Thoughts on New Product and Service Development
- 2.4 Enhanced Customer Experiences
- 2.4.1 Hyper Personalization
- 2.4.2 Customer Support
- 2.4.3 Seamless Interactions
- 2.4.4 Predictive Services
- 2.4.5 Experiential Marketing
- 2.5 Chapter Summary
- 2.6 Key Questions
- The Challenges of Generative AI
- 3.1 Bias
- 3.1.1 What is AI Bias?
- 3.1.2 The Impact of Bias in Generative AI
- 3.1.3 Addressing Bias
- 3.1.4 Interpretability
- 3.2 Security
- 3.2.1 Security Challenges of Generative AI
- 3.2.2 Mitigating Security Risks
- 3.2.3 The Role of Leadership
- 3.3 Regulation
- 3.3.1 Regulatory Landscapes
- 3.3.2 Regulation Areas
- 3.3.3 Implications for Businesses
- 3.3.4 Navigating the Regulatory Maze
- 3.3.5 The Future of AI Regulation
- 3.4 Chapter Summary
- 3.5 Key Questions
- Adopting Generative AI
- 4.1 Develop a Generative AI Strategy
- 4.1.1 Understanding Your Business Context
- 4.1.2 Defining Your AI Vision and Goals
- 4.1.3 Assessing Your AI Capabilities
- 4.1.4 Identifying Use Cases
- 4.1.5 Developing an Implementation Plan
- 4.1.6 Aligning with Ethics and Regulations
- 4.1.7 Building Organizational Readiness
- 4.2 Build a Team with the Right Skills
- 4.2.1 Data Scientists
- 4.2.2 AI Engineers
- 4.2.3 Data Engineers
- 4.2.4 Business Analysts
- 4.2.5 Product Managers
- 4.2.6 UX Designers
- 4.2.7 Ethicists
- 4.2.8 AI Trainers
- 4.2.9 AI Operations Specialists
- 4.2.10 Domain Experts
- 4.3 Select the Right Technology
- 4.3.1 Understanding Your Needs
- 4.3.2 Tool Selection Criteria
- 4.3.3 Popular Frameworks
- 4.3.4 Custom-Built Tools
- 4.3.5 Final Thoughts on Selecting the Right Technology
- 4.4 A Generative AI Deployment
- 4.5 Training Your Employees on Generative AI
- 4.5.1 Understanding the Need for Training
- 4.5.2 Assessing the Current Skill Level
- 4.5.3 Designing the Training Program
- 4.5.4 Blending Theory and Practice
- 4.5.5 Ongoing Training and Support
- 4.5.6 Evaluating the Training Program
- 4.5.7 Final Thoughts on Training
- 4.6 Chapter Summary
- 4.7 Key Questions
- Diving Deeper into Generative AI
- 5.1 Sequence Prediction Capabilities
- 5.1.1 The Basics of Sequence Prediction
- 5.1.2 Sequence Prediction in Generative AI
- 5.1.3 Sequence Prediction Techniques
- 5.1.4 Applications of Sequence Prediction
- 5.1.5 Challenges of Sequence Prediction
- 5.1.6 The Future of Sequence Prediction
- 5.1.7 Final Thoughts on Sequence Predictions
- 5.2 Why Word Embeddings Are Surprisingly Effective
- 5.2.1 The Word Embedding Concept
- 5.2.2 Semantic and Syntactic Relationships
- 5.2.3 Reducing Dimensionality
- 5.2.4 Handling Out-of-Vocabulary Words
- 5.2.5 Transfer Learning and Domain Adaptability
- 5.2.6 Contextualized Word Embeddings
- 5.2.7 Final Thoughts on Word Embeddings
- 5.3 Large Language Models LLMs
- 5.3.1 Understanding LLMs
- 5.3.2 Training LLMs
- 5.3.3 Capabilities of LLMs
- 5.3.4 Fine-Tuning LLMs
- 5.3.5 The Art of Prompt Engineering
- 5.3.6 Data Leakage in Fine-Tuning and Prompt Engineering
- 5.3.7 Shared vs. Private LLMs
- 5.3.8 Hallucinations in LLM Outputs
- 5.3.9 Transparency and Explainability
- 5.3.10 The Power of Transfer Learning
- 5.3.11 Understanding Context with Transformers
- 5.3.12 Handling Ambiguity
- 5.3.13 Challenges and Limitations of LLMs
- 5.3.14 Zero-shot, One-shot, and Few-shot Learning
- 5.3.15 Final Thoughts on LLMs
- 5.4 The Use of Tools with LLMs
- 5.5 Chapter Summary
- 5.6 Key Questions
- Is Generative Content Detectable?
- 6.1 Introduction
- 6.2 Understanding AI and the Motivation for Watermarking
- 6.3 Comparisons with Other Methods of Detection
- 6.4 The Mechanics of Watermarking
- 6.5 Challenges and Countermeasures
- 6.6 Further Developments and Thoughts
- 6.7 Chapter Summary
- 6.8 Key Questions
- A Generative AI Future
- 7.1 The Impact of Generative AI on Society
- 7.2 Will Generative AI be Used for Good?
- 7.3 AI Regulation or More Education?
- 7.4 Chapter Summary
- 7.5 Key Questions
- To Boldly Go...
- 8.1 How Can CEOs Prepare for the Future of Generative AI?
- 8.2 Where to Start Now
- 8.2.1 Marketing
- 8.2.2 Software Engineering
- 8.2.3 Mechanical, Electrical, & Industrial
- Engineering
- 8.2.4 Cybersecurity & Information Technology
- 8.2.5 Sales
- 8.2.6 Finance and Accounting
- 8.2.7 Legal
- 8.3 The Journey Ahead
- 8.4 A Call to Action
- Bibliography
- Index
- About the Author
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