Byte-Sized Learning Series

2024 Edition. Free access to the AI Academy! The perfect guide to help non-technical business leaders understand the power of AI. Completely up to date with the latest advancements in generative AI. Part of the Byte-sized Learning AI series by Now Next Later AI, these books break down complex concepts into easily digestible pieces, providing you with a solid foundation in the fundamentals of AI. More Than a Book   By purchasing this book, you will also be granted free access to the AI Academy platform. There you can view free course modules, test your knowledge through quizzes, attend webinars, and engage in discussion with other readers.   You will also receive free modules and 50% discount toward the enrollment in the self-paced course of the same name and enjoy video summary lessons, instructor-graded assignments, and live sessions. A course certificate will be awarded upon successful completion.   AI Academy by Now Next Later AI   We are the most trusted and effective learning platform dedicated to empowering leaders with the knowledge and skills needed to harness the power of AI safely and ethically.   Book and Course Learning Rubric   - Chapters 1-7: Understanding of AI [11%] —Demonstrated comprehension of AI's evolution, definition, applications, and comparison with human intelligence. - Chapters 8-13: Understanding of Data and Data Management [11%] — Clear understanding of the significance of big data, and strategies for data management. - Chapters 14-29: Understanding of Machine Learning [30%] — Familiarity with machine learning algorithms, different learning types, and the key steps involved in a machine learning project. - Chapters 30-35: Understanding of Deep Learning [9%] — Understanding of deep learning, its basics, and the structure and types of neural networks. - Chapters 36-40: Understanding of Model Selection and Evaluation [9%] — Ability to select and evaluate machine learning models and utilize them for decision-making. - Chapters 41-50: Understanding of Generative AI [15%] — Detailed understanding of generative AI, its value chain, models, prompt strategies, applications, opportunities, and governance challenges. - Assignment: Practical Application [15%] — Ability to apply generative AI understanding to real-world business challenges, demonstrating critical thinking and strategic planning skills.  

This practical and concise guide provides senior decision-makers with a clear, accessible roadmap for harnessing the power of generative AI, enhancing innovation, and boosting business outcomes.    Drawing on insights from AI-enabled business transformations in diverse sectors, it presents a validated strategic approach. This blueprint not only outlines best practices but also showcases pioneering use cases, integrating them into a cohesive framework for practical implementation. This scenario-based approach helps leaders understand where and how to apply the practices outlined.   Spanning across areas from strategic alignment and talent development to ethical governance and sustaining a competitive edge amid relentless underlying progress, it delivers clarity for charting an optimal Generative AI roadmap.   This book is designed for busy executives and can be read in less than two hours. For a more in-depth exploration of Generative AI for business, check out our book "Introduction to Large Language Models for Business Leaders: Responsible AI Strategy Beyond Fear and Hype."   Who this Book is For   The core audience comprises senior executives like CEOs, Transformation advisors, strategic planners, technology heads, product leaders or functional unit heads keen on harnessing generative AI for a competitive edge but needing authoritative counsel consolidating recent lessons into a crisp actionable package to aid planning.   Key Topics Covered   Understanding Generative AI: What it is, key capabilities and applications, strengths and limitations. Strategic Alignment: Mapping generative AI to business goals, prioritizing high-impact use cases, managing risks. Talent and Skills: Developing in-house capabilities through upskilling programs, attracting and retaining external AI talent. Technology Integration: Assessing IT infrastructure readiness, optimizing make vs buy decisions for AI solutions. Implementation and Scaling: Pilot testing for viability, expanding validated applications through metrics-driven scaling. Risk Management and Ethics: Governing biases and reliability issues, safeguarding data privacy and security. Organizational Change: Securing leadership commitment, preparing the workforce to adopt new AI-powered processes. Continuous Improvement: Quantifying value through KPIs, optimizing models through responsive feedback loops. Future-Proofing: Investing in R&D for sustained innovation, building agility to adapt to rapid AI progress.   Featuring use studies, business scenarios, practical frameworks, and research insights from top consultancies and AI leaders, this book delivers a visionary yet pragmatic roadmap for AI transformation. A must-read guide for any organization looking to leverage generative AI for competitive advantage.   More Than a Book   By purchasing this book, you will also be granted free access to the AI Academy platform. There you can view free course modules, test your knowledge through quizzes, attend webinars, and engage in discussion with other readers. No credit card required.   AI Academy by Now Next Later AI   We are the most trusted and effective learning platform dedicated to empowering leaders with the knowledge and skills needed to harness the power of AI safely and ethically.

Responsible AI Strategy Beyond Fear and Hype - 2024 Edition   Finalist for the 2023 HARVEY CHUTE Book Awards recognizing emerging talent and outstanding works in the genre of Business and Enterprise Non-Fiction.   In this comprehensive guide, business leaders will gain a nuanced understanding of large language models (LLMs) and generative AI. The book covers the rapid progress of LLMs, explains technical concepts in non-technical terms, provides business use cases, offers implementation strategies, explores impacts on the workforce, and discusses ethical considerations. Key topics include: The Evolution of LLMs: From early statistical models to transformer architectures and foundation models. How LLMS Understand Language: Demystifying key components like self-attention, embeddings, and deep linguistic modeling. The Art of Inference: Exploring inference parameters for controlling and optimizing LLM outputs. Appropriate Use Cases: A nuanced look at LLM strengths and limitations across applications like creative writing, conversational agents, search, and coding assistance. Productivity Gains: Synthesizing the latest research on generative AI's impact on worker efficiency and satisfaction. The Perils of Automation: Examining risks like automation blindness, deskilling, disrupted teamwork and more if LLMs are deployed without deliberate precautions. The LLM Value Chain: Analyzing key components, players, trends and strategic considerations. Computational Power: A deep dive into the staggering compute requirements behind state-of-the-art generative AI. Open Source vs Big Tech: Exploring the high-stakes battle between open and proprietary approaches to AI development. The Generative AI Project Lifecycle: A blueprint spanning use case definition, model selection, adaptation, integration and deployment. Ethical Data Sourcing: Why the training data supply chain proves as crucial as model architecture for responsible development. Evaluating LLMs: Surveying common benchmarks, their limitations, and holistic alternatives. Efficient Fine-Tuning: Examining techniques like LoRA and PEFT that adapt LLMs for applications with minimal compute. Human Feedback: How reinforcement learning incorporating human ratings and demonstrations steers models towards helpfulness. Ensemble Models and Mixture-of-Experts: Parallels between collaborative intelligence in human teams and AI systems. Areas of Research and Innovation: Retrieval augmentation, program-aided language models, action-based reasoning and more. Ethical Deployment: Pragmatic steps for testing, monitoring, seeking feedback, auditing incentives and mitigating risks responsibly. The book offers an impartial narrative aimed at informing readers for thoughtful adoption, maximizing real-world benefits while proactively addressing risks. With this guide, leaders gain integrated perspectives essential to setting sound strategies amidst generative AI's rapid evolution.   More Than a Book   By purchasing this book, you will also be granted access to the AI Academy platform. There you can view course modules, test your knowledge through quizzes, attend webinars, and engage in discussion with other readers. 

2024 Edition. Business leaders must understand how to effectively leverage Generative AI within their companies in order to remain competitive. This book collection offers a fresh, timely viewpoint on this critical topic. Readers will gain foundational knowledge about AI and Generative algorithms while exploring both the potential benefits, risks and ethics involved. Guidance is provided on enhancing an organization's offerings, operating model and strategic direction while mitigating biases and negative consequences.   The book collection lays out a comprehensive approach for businesses to successfully adopt and integrate Generative AI technologies. Common errors and challenges that companies face in this relatively new domain are highlighted, along with proven tactics to overcome them and achieve strong results.   A comprehensive playbook to unlock the commercial potential of generative AI for managers, directors, executives, governance specialists, and any professionals interested in the intersection of business and emerging technologies.   Book: Generative AI Transformation Blueprint   Drawing on insights from AI-enabled business transformations in diverse sectors, it presents a validated strategic approach. This blueprint not only outlines best practices but also showcases pioneering use cases, integrating them into a cohesive framework for practical implementation. This scenario-based approach helps leaders understand where and how to apply the practices outlined.   Spanning across areas from strategic alignment and talent development to ethical governance and sustaining a competitive edge amid relentless underlying progress, it delivers clarity for charting an optimal Generative AI roadmap.   Book: Introduction to Large Language Models for Business Leaders: Responsible AI Strategy Beyond Fear and Hype   Finalist for the 2023 HARVEY CHUTE Book Awards recognizing emerging talent and outstanding works in the genre of Business and Enterprise Non-Fiction.   Explore the transformative potential of technologies like GPT-4 and Claude 2. These large language models (LLMs) promise to reshape how businesses operate. Aimed at non-technical business leaders, this guide offers a pragmatic approach to leveraging LLMs for tangible benefits, while ensuring ethical considerations aren't sidelined.   LLMs can refine processes in marketing, software development, HR, R&D, customer service, and even legal operations. But it's essential to approach them with a balanced view. In this guide, you'll: Learn about the rapid advancements of LLMs. Understand complex concepts in simple terms. Discover practical business applications. Get strategies for smooth integration. Assess potential impacts on your team. Delve into the ethics of deploying LLMs.     Book: Artificial Intelligence Fundamentals for Business Leaders: Up to Date With Generative AI   The perfect guide to help non-technical business leaders understand the power of AI: Machine Learning, Neural Networks, and Data Management. Up to date with Generative AI.   More Than a Book Collection   By purchasing this series, you will also be granted access to the AI Academy platform. There you can test your knowledge through end-of-chapter quizzes and engage in discussion.   You will also be able to watch course modules and receive 50% discount toward the enrollment in the self-paced course of the same name and enjoy video summary lessons, instructor-graded assignments, and live sessions. A course certificate will be awarded upon successful completion.

In "Responsible AI in the Age of Generative Models: Governance, Ethics and Risk Management" we present a comprehensive guide to navigating the complex landscape of ethical AI development and deployment. As generative AI systems become increasingly powerful and ubiquitous, it is crucial to develop governance frameworks that mitigate potential risks while harnessing the technology's transformative potential. This book presents a rights-based approach, grounded in established human rights frameworks, to align AI systems with societal values and expectations.   Divided into ten parts, the book covers a wide range of topics essential for responsible AI governance:   Maps generative AI risks to specific human rights. Presents a framework for institutionalizing rights-respecting AI practices throughout the development lifecycle. Delves into responsible data governance practices. Examines participatory approaches to data stewardship. Explores the roles and responsibilities of different organizational functions in operationalizing responsible AI, emphasizing the need for cross-functional collaboration. Focuses on transparency and algorithmic auditing. Provides guidance on implementing effective multi-layered governance across the AI system lifecycle. Introduces maturity models for assessing an organization's responsible AI capabilities. Features an in-depth case study of Anthropic's innovative Constitutional AI approach. Analyzes emerging regulatory frameworks such as the EU AI Act and discusses the implications for businesses operating in multiple jurisdictions.   "Responsible AI in the Age of Generative Models" equips readers with the knowledge, tools, and strategies needed to unlock the transformative potential of generative models while safeguarding human rights and promoting social justice. It is an essential resource for business leaders, policymakers, researchers, and anyone concerned about the future of AI governance.   By embracing responsible AI as an imperative, we can work together to build a world where AI empowers and uplifts us all. This book is an invitation to engage in that critical conversation and take action towards a more equitable future.