Reading AI Research
From arXiv scroll to actual understanding
How to read ML papers efficiently, the landmark papers that built modern AI, and how to evaluate claims skeptically.
Intermediate3 modules · 11 lessons~99 min1000 XPAligned with The landmark papers of modern AIStart with: ML Foundations
Progress0/11
Module
How to Read a Paper
Anatomy, the three-pass method, and the red flags that expose weak claims
Anatomy of an ML Paper8 min · 80 XPAnatomy of an ML Paper
The Three-Pass Method9 min · 90 XPThe Three-Pass Method
Red Flags in ML Papers9 min · 90 XPRed Flags in ML Papers
Module
The Landmark Papers
What each showed, why it mattered, and how to spot its fingerprints today
AlexNet (2012)8 min · 80 XPAlexNet (2012)
Attention Is All You Need (2017)10 min · 100 XPAttention Is All You Need (2017)
BERT & the Pretrain–Fine-Tune Era (2018)9 min · 90 XPBERT & the Pretrain–Fine-Tune Era (2018)
GPT-2 → GPT-3 & the Scaling Laws (2019–2022)10 min · 100 XPGPT-2 → GPT-3 & the Scaling Laws (2019–2022)
InstructGPT & Chain-of-Thought (2022)10 min · 100 XPInstructGPT & Chain-of-Thought (2022)
Module
Keeping Up
The arXiv firehose, filtering strategies, and building a reading practice that survives
The arXiv Firehose8 min · 80 XPThe arXiv Firehose
Filtering the Signal9 min · 90 XPFiltering the Signal
Reproducing & Building a Practice9 min · 100 XPReproducing & Building a Practice
Capstone project
Read a real paper
Pick one influential AI paper (attention, scaling laws, RLHF — or one you find). Read it properly and produce a summary a smart teenager could understand.
Deliverable: A structured summary: the problem, the key idea, the evidence, and why it mattered.
Milestones
Finish every lesson to earn the AI Research Literacy Certificate.