📅 2025-02-27 — Session: Explored ethical and AI techniques for thesis
🕒 15:10–15:35
🏷️ Labels: Ethics, Deep Learning, Thesis, Data Privacy, Ai Techniques
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The session aimed to explore both ethical considerations in data sharing and advanced AI techniques relevant to a thesis project.
Key Activities
- Ethics of Data Sharing: Analyzed the ethical implications of making census data public, discussing benefits and risks, and recommending responsible data use.
- Ethical Data Use: Outlined ethical and legal considerations for using publicly available data, emphasizing researcher responsibilities.
- Greedy Layer-Wise Pretraining: Evaluated its relevance for a thesis on deep learning, focusing on training dynamics and learning approaches.
- Transfer Learning: Assessed its applicability for a thesis use case involving structured data and machine learning.
- Regularization Strategies: Reviewed strategies for population-structured data, identifying suitable and unsuitable techniques.
- Hinton et al. (2006) Paper: Evaluated its relevance to structured population data, focusing on deep learning applications.
Achievements
- Clarified the ethical responsibilities in data sharing and usage.
- Identified relevant AI techniques for thesis work, including greedy layer-wise pretraining and transfer learning.
Pending Tasks
- Further exploration of specific AI techniques for thesis application.
- Develop a comprehensive ethical framework for data usage in research.