My AI Research Program

🌈 Abstract

The article outlines a project-driven AI research program to deeply understand architectures, new age applications, research directions, and learn AI concepts from first principles. The author aims to reverse engineer the pathway to the job of an AI Research Engineer at top AI labs like Meta AI, OpenAI, and Anthropic.

🙋 Q&A

[01] Designing a Research Program

1. What are the key pillars of the author's AI Research Program? The author's AI Research Program is built on several key pillars:

• Foundational Knowledge - Core mathematical and theoretical concepts underlying AI and machine learning
• Programming and Tools - Essential programming skills and tools for AI development and deployment
• Deep Learning Fundamentals - Key concepts and architectures in deep learning and neural networks
• Reinforcement Learning - Principles and applications of reinforcement learning in AI
• NLP / LLM Research - Advanced topics in natural language processing and large language models
• Research Paper Analysis and Replication - Skills for critically analyzing and reproducing cutting-edge AI research
• High-Performance AI Systems and Applications - Techniques for optimizing and scaling AI systems for real-world applications
• Large-scale ETL and Data Engineering - Methods for handling and processing large-scale data for AI applications
• Ethical AI and Responsible Development - Ethical considerations and responsible practices in AI development
• Community Engagement and Networking - Building connections and contributing to the AI research community
• Research and Publication - Conducting original AI research and sharing findings through academic publications

2. How does the author recommend going through this AI Research Program? The author suggests the following approach:

• Assess your starting point and determine your current knowledge level in each pillar
• Craft a project and goal for yourself, defining what you want to achieve
• Learn by doing - build your projects end-to-end in an iterative manner, going back to learning resources as needed
• Engage with the AI community through forums, Discord servers, conferences, etc.
• Write and teach about your learnings to expose gaps in your understanding and solidify your knowledge

3. What is the author's first project for kickstarting this program? The author's first project is "Building and Analyzing a Miniature Language Model from Scratch". The key objectives are:

• Develop a deep understanding of LLM research and engineering
• Implement and optimize the training process for a smaller-scale language model
• Explore core concepts like transformer architectures, embedding models, attention mechanisms, dataset preparation, and hardware/compute requirements

[02] Sustaining the Mindset for Learning

1. What are the author's tips for sustaining the motivation to learn throughout this program? The author's tips for sustaining the motivation to learn include:

• Don't over-exert out of excitement, cycle through periods of intensity and relaxed learning
• Commit publicly to keep yourself accountable
• Adopt a "No Zero Days" approach to ensure consistent progress
• Find and connect with other people on a similar learning journey