1. Machine Learning System Design for RAG
Design a machine learning system for a Retriever-Augmented Generator (RAG). Discuss the design considerations and interact with the interviewer to explore the system's architecture and components.
2. ML Design: Bootstrapping from Unlabeled Data
Describe how you would design a machine learning system to mine new insights from a large volume of unlabeled data, mentioning the use of bootstrapping techniques.
3. Debugging Multithreaded MLP Training Code
Given a complete piece of code and test cases for training a simple Multilayer Perceptron (MLP) using multithreading, identify and debug any issues present.
4. Discuss AI Safety
During the interview process, you were asked an uncommon question about AI safety. Could you discuss your understanding of AI safety and its importance in the development and deployment of AI systems?
5. Basic Linear Algebra and Back-propagation Knowledge Assessment
The interview will assess basic linear algebra and back-propagation knowledge. You are required to have knowledge of linear algebra and neural network training. Can you explain the key concepts of linear algebra that are essential for understanding neural network training and demonstrate how back-propagation works in the context of training neural networks?
