Resources in learning AI — Karthikeshwar

1. Legendary papers and blogs

Richard Sutton’s essay Bitter Lesson: General methods leveraging computation and learning, rather than human-designed knowledge or rules, are the most effective path to achieving significant progress in AI.
Attention Is All You Need: The landmark Google paper introducing transformers based on attention mechanism.
Dario Amodei’s (Co-founder & CEO of Anthropic) essay Machines of Loving Grace
Many-Shot In-Context Learning:
- With enough examples, many-shot ICL (In-Context Learning) can match or even surpass the performance of fine-tuned models on several tasks.
- Many-shot ICL can override pretraining biases and learn high-dimensional functions (e.g., numerical tasks) where few-shot ICL struggles.

Deep Learning by Aaron Courville, Ian Goodfellow, and Yoshua Bengio: The Deep Learning Bible
Hands-On ML with Scikit-Learn, Keras and TensorFlow by Aurelien Geron
Alice in a Differentiable Wonderland
Artificial Intelligence: A Modern Approach by Peter Norvig and Stuart J. Russell