Generative AI For Leaders
1st Edition
Published on 13. September 2023
160 pages
979-8-9884751-6-3 (ISBN)
Description
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.
More details
Person
Amir Husain is the Founder and CEO of SparkCognition. He has been named Austin's Top Technology Entrepreneur of the Year, listed as an Onalytica Top 100 Artificial Intelligence Influencer, and received the Austin Under 40 Technology and Science Award, among other accolades recognizing his work in artificial intelligence. He serves on the Board of Advisors for The University of Texas at Austin Department of Computer Science and on the NATO Maritime Unmanned Systems Innovation Advisory Board. He is also a member of the Council on Foreign Relations. Husain has been awarded over 30 patents and has several pending applications. His work has been featured in world-leading outlets such as Foreign Policy, Fox Business News, and Proceedings from the U.S. Naval Institute. He is the author of the best-selling book "The Sentient Machine: The Coming Age of Artificial Intelligence" and a co-author of the compilation "Hyperwar: Conflict and Competition in the AI Century."
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
