FRANKI TABOR, FRSA

Ethical Data Harvesting for Customer Data Platforms: Unlocking the Power of Bidirectional Conversational AI, explores the intersection of AI technology, ethical data practices, and regulatory compliance. It emphasizes the importance of designing secure, scalable, and compliant AI-driven interactions that enhance real-time insights and personalization while prioritizing user trust and transparency.

Key takeaways include:

Importance of Ethical Practices: Organizations must… 展开

Ethical Data Harvesting for Customer Data Platforms: Unlocking the Power of Bidirectional Conversational AI, explores the intersection of AI technology, ethical data practices, and regulatory compliance. It emphasizes the importance of designing secure, scalable, and compliant AI-driven interactions that enhance real-time insights and personalization while prioritizing user trust and transparency.

Key takeaways include:

Importance of Ethical Practices: Organizations must prioritize ethical data harvesting practices to build trust and ensure compliance with regulations while leveraging AI technologies in customer interactions.

Understanding Regulatory Compliance: Familiarity with regulations such as GDPR, CCPA, and COPPA is crucial for organizations to navigate legal obligations and protect user data rights.

Transparency and Trust: Clear communication regarding data collection, AI capabilities, and the nature of interactions with virtual avatars is essential for fostering customer trust.

User Empowerment: Providing customers with control over their data, including consent management and the right to access and delete information, enhances their confidence in engaging with AI systems. 收起

As AI and data technologies advance, organizations face complex risks, regulations, and ethical challenges. "Manage AI & Data Risk: Effective Strategies for Governing Information, Data & Artificial Intelligence" serves as a comprehensive guide for leaders, professionals, and policymakers to navigate AI's challenges while maintaining ethical and compliant data governance.

The book starts by explaining the fundamentals of AI, from machine learning to the evolving AI tech stack, offering… 展开

As AI and data technologies advance, organizations face complex risks, regulations, and ethical challenges. "Manage AI & Data Risk: Effective Strategies for Governing Information, Data & Artificial Intelligence" serves as a comprehensive guide for leaders, professionals, and policymakers to navigate AI's challenges while maintaining ethical and compliant data governance.

The book starts by explaining the fundamentals of AI, from machine learning to the evolving AI tech stack, offering readers insights into the development of AI and data science. It then explores the risks posed by AI systems and compares various global ethical frameworks for trustworthy AI use.

With a focus on regulatory landscapes like the EU's GDPR and the emerging EU AI Act, the guide provides an in-depth look at AI risk management frameworks to ensure compliance with current and future laws. It also breaks down the AI life cycle, offering practical insights on structuring, testing, and validating AI projects.

Actionable strategies are provided for responsible AI governance, including integrating AI risk management with operational risk strategies, establishing governance infrastructure, managing post-deployment systems, and addressing legal and auditing challenges.

"Manage AI & Data Risk" equips organizations to govern AI and data systems effectively, mitigate risks, ensure compliance, and build stakeholder trust—making it essential for business leaders, data scientists, risk managers, and policymakers. 收起

In today’s fast-paced digital marketplace, personalized customer experiences are no longer a luxury—they are a necessity. E-commerce businesses that fail to deliver relevant, timely, and personalized product recommendations risk losing customers to more innovative competitors. AI-Powered Commerce: Revolutionizing Product Recommendations with LLMs and RAG provides executives with the blueprint to harness the transformative power of Large Language Models (LLMs) and Retrieval-Augmented Generation… 展开

In today’s fast-paced digital marketplace, personalized customer experiences are no longer a luxury—they are a necessity. E-commerce businesses that fail to deliver relevant, timely, and personalized product recommendations risk losing customers to more innovative competitors. AI-Powered Commerce: Revolutionizing Product Recommendations with LLMs and RAG provides executives with the blueprint to harness the transformative power of Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) to revolutionize how products are recommended to customers in real-time, leading to substantial growth in revenue, customer satisfaction, and operational efficiency.

Key takeaways include:
- Understanding how LLMs and RAG can revolutionize product recommendations.
- Building AI-powered conversational systems that scale across platforms.
- Real-world case studies demonstrating successful AI implementation in e-commerce.
- Strategies for overcoming technical, operational, and compliance challenges.
- How to maximize business growth through AI-driven customer engagement and personalization.
In a world where customer expectations continue to evolve, this book will help you lead the charge in AI-powered commerce, ensuring your business stays ahead of the competition in delivering exceptional, data-driven customer experiences." 收起

Beyond Words offers a comprehensive exploration of Large Language Models (LLMs), delving into their development, architecture, applications, and the broader implications of their use. This book is designed for both newcomers and seasoned professionals interested in understanding the transformative impact of LLMs on various fields.

Key Features:
* Performance and Capabilities: The book critically evaluates the performance of LLMs, comparing them with other generative models like GANs… 展开

Beyond Words offers a comprehensive exploration of Large Language Models (LLMs), delving into their development, architecture, applications, and the broader implications of their use. This book is designed for both newcomers and seasoned professionals interested in understanding the transformative impact of LLMs on various fields.

Key Features:
* Performance and Capabilities: The book critically evaluates the performance of LLMs, comparing them with other generative models like GANs and VAEs, and offers insights into benchmarking, strengths, and limitations.
* Training and Fine-tuning: Readers gain an understanding of the data collection, training processes, hyperparameter tuning, and the computational resources required for developing LLMs. The book also explores methods of fine-tuning LLMs for specific applications and domains.
* Key Challenges and Limitations: Tabor addresses the ethical, resource consumption, interpretability, and handling of ambiguity challenges associated with LLMs, providing a balanced view of the technology's limitations and trade-offs.
* Future Directions and Trends: The book looks ahead at emerging architectures, advances in training methods, and potential future applications, offering predictions about the long-term impact of LLMs on AI research and industry.
* Case Studies and Practical Applications: Real-world case studies illustrate the practical applications of LLMs across various sectors, including natural language processing, content generation, conversational AI, and translation. 收起