Atlas · GenAI 2026
Research-to-Engineering Translation
Translating arXiv papers into actionable engineering decisions
conceptPeak: 2024Applied Research PracticeAI consensus: 1/3
Prerequisites
- hardDeep Learning
Reading ML papers requires understanding the notation, architectures, and training procedures described — DL is the language of the papers
- mediumStatistical Inference
Papers contain ablation studies, significance tests, and confidence intervals — statistical literacy helps assess claims critically
Recommended reference
Karpathy, A. (2019) 'A Recipe for Training Neural Networks' — karpathy.github.io; meta-guide on going from paper to implementation
Notes from AI deep research
Anthropic Opus
Karpathy (2019) Recipe. Paper → implementable core → cost/benefit → adopt w dni
Related skills
- → is part of: AI Product Management(3/3)
- → is part of: Data Science(2/3)
- ← is subcategory of: Scientific Writing(0/3)