Next Gen 12-3 @Data Protection for AI
Next Gen 12-3 @Data Protection for AI
This framework sets the stage for an engaging discussion on the future of data in AI, focusing on the transition to localized data practices and the implications for data protection.
Language: Cantonese
Agenda:
6:30 PM - Reception
7:00 -9:00PM - Sharing and panel Session
Ticket Fee: HK$ 150 (Includes TWO complementary drinks)
Guest Speakers
Mr. Frankie Leung
Mr. Leung is an independent IT Security Consultant. He has over 37 years well-rounded IT management experience in Technical Product Marketing, Business Information Management, Software Development as well as Information Security Consulting in Greater China region and many Asian countries. As an independent Security Consultant, he has provided different security solutions, technical write up, defining Security policies, IT audit , Computer Forensic and Security Awareness Training to major government departments, High Education Collage and some large finance institutes in Asia, particularly for USB Device Controls, Encryption Technology, Network Security, PKI infra-structure, Multi-factor Authentication, Digital Rights Management, Data Leakage Prevention, Cloud Security, IT Audit and Computer Forensic.
Mr. Leung is now the Chairperson of Professional Information Security Association and Chairman of HK CTF Association. He is the credential holder of CISSP, CISA, CISM< CRISC and CDPSE and also the CISSP, CISA, CISM training course instructor.
Mr. Neith Hu
Neith Hu is a highly experienced Corporate Trainer with over 20 years in the field. Recently, he designed and delivered a cutting-edge sales training program for a leading AI startup in Hong Kong. This program, titled "AI in Action," focused on the practical application of the latest AI solutions to drive measurable results, encapsulated by the concept of "AI into ROI."
Key highlights of Neith's training include:
Integration of AI Solutions: Practical strategies for implementing AI tools in sales processes.
Sales Techniques: Utilization of proven methods developed by Dr. John Koo to enhance sales effectiveness.
Measurable Outcomes: Emphasis on achieving tangible results through innovative practices.
Neith is eager to share his experiences and success stories, helping others leverage AI to transform their sales strategies.
Introduction
When discussing AI, particularly large language models (LLMs), the relationship between data and model performance is crucial. Here’s a breakdown of how these elements interact:
1. Importance of Data
Training Data: LLMs are trained on vast datasets that include text from books, articles, websites, and more. The quality and diversity of this data directly impact the model's understanding and performance.
Data Quantity: More data typically leads to better performance, as the model can learn from a wider range of examples and contexts.
2. Types of Data Used
Textual Data: Includes natural language text from diverse sources, allowing the model to learn language patterns, grammar, and context.
Domain-Specific Data: Incorporating specialized data (e.g., medical, legal) can improve the model’s accuracy in specific fields.
3. Data Preprocessing
Cleaning: Removing irrelevant or low-quality data helps improve model training outcomes.
Tokenization: Breaking down text into tokens that the model can understand, which is essential for effective training.
4. Ethical Considerations
Bias: Training data can contain biases, which LLMs may inadvertently learn and perpetuate. Careful curation and evaluation of data are necessary to mitigate this.
Privacy: Ensuring that the training data complies with data protection regulations and does not include sensitive personal information is crucial.
5. Continuous Learning
Fine-Tuning: After initial training, LLMs can be fine-tuned on specific datasets to improve performance in targeted applications.
Feedback Loops: Incorporating user feedback can help refine the model over time.
6. Data Localization
Localized data can enhance the relevance and effectiveness of LLMs by tailoring responses to specific cultural and contextual needs.
Conclusion
Data is the backbone of AI and LLMs. The quality, diversity, and ethical handling of data are vital for developing effective, fair, and responsible AI systems.