A living shelf for understanding modern AI, the papers that started it, the books that make sense of it, and the essays worth arguing with. Where a topic has an explainer here, it’s linked.
Papers that made the moment
- Attention Is All You Need (Vaswani et al., 2017), the transformer. See the interactive version: How a transformer thinks.
- Language Models are Few-Shot Learners (Brown et al., 2020), scale as a capability unlock.
- Deep Residual Learning (He et al., 2015), the trick that let networks get deep.
- Word2Vec (Mikolov et al., 2013), the origin of the embedding arithmetic party trick.
Books
To read
- Why Machines Learn by Anil Ananthaswamy, the maths of ML, gently.
- The Alignment Problem by Brian Christian, where values and optimization collide.
- Co-Intelligence by Ethan Mollick, living and working alongside models.
Read
- Atlas of AI by Kate Crawford, the material and political cost of AI.
- The Coming Wave by Mustafa Suleyman, containment and consequence.
- You Look Like a Thing and I Love You by Janelle Shane, why AI is weirder (and dumber) than it looks.
- Life 3.0 by Max Tegmark, futures worth steering toward.
Essays & explorables I return to
- Chris Olah et al., Distill, the gold standard for explaining ML visually.
- Andrej Karpathy, The Unreasonable Effectiveness of Recurrent Neural Networks.
- Everything in this garden’s own concept notes.