Hi Everyone,
I wanted to start a list of books, courses, papers that would be useful for anyone getting started in AI, or wants to get a list of some read some landmark papers, tutorials, etc. Will try my best to keep this post updated as I see more resources:
AI In Space Biology:
Piggybacking on Ryan’s Post earlier:
How can AI and machine learning transform space biology research? @AIMLawg
This AI/ML in Space Biology Training self-paced course is designed for researchers and combines foundational space biology with hands-on experience in data processing and machine learning. Equip yourself with essential skills for complex biological questions in space.
More info, background: Training Resources - AI/ML for Space Biology - NASA Science
Register yourself for the course here Enroll in NASA TOPS-T ScienceCore AI/ML in Space Biology Training
AI Books that Casual Readers Might Appreciate:
- The Worlds I See - Dr. Fie Fie Li - talks about “human-centered AI,” arguing that technology must be developed with a focus on human dignity, ethics, and benefit rather than just technical performance.
- AI Snake Oil - Arvind Narayanan and Sayash Kapoor - critiques the tech industry’s inflated promises and exposes the “hype vortex” surrounding artificial intelligence.
- Weapons of Math Destruction - Cathy O’Neil - explains how algorithmic bias reinforces social inequality.
- Code Dependent
Websites That Explain Algorithms Visually:
- https://ml-visualiser.vercel.app/
- https://mlu-explain.github.io/
- https://www.r2d3.us/visual-intro-to-machine-learning-part-1/
AI Papers:
- ImageNet: A Large-Scale Hierarchical Image Database (Deng et al., 2009)
- ImageNet Classification with Deep Convolutional Neural Networks (Krizhevsky, Sutskever, & Hinton, 2012): Often called the “AlexNet” paper,
- Deep Learning (LeCun, Bengio, & Hinton, 2015)
- Attention Is All You Need (Vaswani et al., 2017)
- Language Models are Few-Shot Learners (Brown et al., 2020)
- Training Compute-Optimal Large Language Models (Hoffmann et al., 2022): Known as the “Chinchilla” paper
- Constitutional AI: Harmlessness from AI Feedback (Bai et al., 2022)
- LoRA: Low-Rank Adaptation of Large Language Models (Hu et al., 2021)
- Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture (Assran et al., 2023)
Related Biology-AI Landmark Papers:
- Evolutionary-scale prediction of atomic-level protein structure with a language model (Lin et al., 2023)
- Sequence modeling and design from molecular to genome scale with Evo (Nguyen et al., 2024)
- scGPT: toward a foundation model for single-cell biology (Cui et al., 2024)
- The most recent frontier (2025–2026) is the “Virtual Cell,” where AI integrates multiple types of biological data into a single representation
AI Resources to Read and Understand Papers:
- NotebookLM - This is free for students - sign up with your student ID and read away to glory
- ResearchRabbit: Described as a “Spotify for papers,” it uses a recommendation engine to find related research based on your existing collections
- Elicit: Specializes in extracting specific data points (like methodology, sample size, or outcomes) from a large body of literature into structured comparison tables.
- Obsidian: A privacy-focused, offline-first tool using markdown files. Its “Graph View” allows you to visualize connections between your personal notes as a network.