The 7 Papers Every AI PM Must Know
If you only read 7 papers in your life, make it these. The foundations of modern AI — architecture, scale, alignment, and efficiency.
Ashish Vaswani et al.
Transformers revolutionize AI by ditching recurrence and convolutions, shining with sheer parallelizable efficiency.
Why this paper
The paper that changed everything. No other architecture paper has had more commercial impact.
BERT revolutionizes NLP by learning context from both directions, improving accuracy across key benchmarks.
Why this paper
Bidirectional understanding — the approach behind Google Search's biggest upgrade in decades.
gpt-3 — coming soon
The commercial turning point. Proves scale alone produces emergent intelligence.
Jared Kaplan et al.
Larger language models offer more sample efficiency, enabling better results with smaller datasets and fixed compute resources.
Why this paper
The scientific foundation for why OpenAI, Google, and Anthropic keep building bigger models.
instructgpt — coming soon
RLHF in practice — the exact technique that created ChatGPT from GPT-3.
Edward Hu et al.
LoRA slashes fine-tuning costs by 10,000x and GPUs by 3x while preserving quality on large language models.
Why this paper
The efficiency breakthrough that made custom AI models accessible to any team.
Hugo Touvron et al.
Llama 2 outperforms open-source chat models, challenging its closed-source rivals in safety and dialogue optimization.
Why this paper
Open source changed the competitive landscape. Llama-2 forced every lab to rethink their strategy.
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