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Papers that changed AI, with a plain-language explainer for each.
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- 2026
Muse Spark Safety & Preparedness Report
arXiv
Meta's safety and preparedness report for Muse Spark, the model behind Meta AI, sets out the catastrophic-risk evaluations behind its launch and argues that, with mitigations applied, residual risk is acceptable.
- 2022
Constitutional AI: Harmlessness from AI Feedback
arXiv
Constitutional AI, a 2022 paper from Anthropic, proposes training a harmless AI assistant with AI-generated feedback guided by a written set of principles, instead of human labels of harmful outputs.
- 2022
Holistic Evaluation of Language Models (HELM)
arXiv
A 2022 paper by Percy Liang, Rishi Bommasani, Tony Lee and many co-authors that ran 30 language models through the same 42 scenarios and seven metrics, turning scattered benchmarks into one standardized evaluation.
- 2022
Training language models to follow instructions with human feedback (InstructGPT)
NeurIPS
InstructGPT used human-written demonstrations, human rankings of outputs and reinforcement learning to make GPT-3 follow user instructions better. Labelers preferred its outputs even though it was far smaller than GPT-3.
- 2021
On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?
FAccT '21
A 2021 FAccT paper by Bender, Gebru, McMillan-Major and Shmitchell that asks whether language models can be too large and sets out recommendations on costs, data, research goals and alternative directions.
- 2019
Language Models are Unsupervised Multitask Learners (GPT-2)
OpenAI
OpenAI's 2019 paper on GPT-2, a 1.5-billion-parameter Transformer trained on web text. It showed that a model trained only to predict the next word could perform several tasks when prompted, and its staged release sparked public debate.
- 2017
Proximal Policy Optimization Algorithms
arXiv
A 2017 paper by Schulman, Wolski, Dhariwal, Radford and Klimov that introduced proximal policy optimization (PPO), a reinforcement learning method that is much simpler to implement and tune and became OpenAI's default RL algorithm.
- 2017
Deep reinforcement learning from human preferences
NeurIPS
A 2017 paper showing that an AI agent can learn a task from short human comparisons instead of a hand-written reward. The method, reinforcement learning from human feedback, later became central to training chat assistants.
- 1990
Elephants Don't Play Chess
Rodney Brooks's 1990 paper argued for building artificial intelligence from the ground up, with ongoing physical interaction with the environment as the main constraint on design, rather than through the abstract manipulation of symbols that had dominated the field for roughly thirty years.
- 1988
ALVINN: An Autonomous Land Vehicle in a Neural Network
ANIPS11
A 3-layer back-propagation network that takes camera and laser range finder images and outputs the direction to steer, trained on simulated road images and tested on Carnegie Mellon's autonomous navigation test vehicle.