The archive · 11 labs · 1,214 dispatches

The labs

No more opening a dozen official blogs every morning. First-hand releases from OpenAI, Anthropic, DeepMind and the rest, each with its substance pulled out.

1051

Generative Language Modeling for Automated Theorem Proving

OpenAI introduces GPT-f, a transformer-based language model designed for the Metamath formalization language that successfully contributed new proofs to the main Metamath library.

1052

Learning to Summarize with Human Feedback

OpenAI demonstrates that reinforcement learning from human feedback (RLHF) allows smaller language models to outperform significantly larger models trained only via supervised learning in text summarization.

1053

OpenAI Scholars 2020 Final Projects

OpenAI announced the final projects of the 2020 Scholars program, showcasing research in neural network interpretability, reinforcement learning, semantic parsing, and medical AI.

1054

OpenAI Procgen and MineRL Competitions

OpenAI is co-organizing two NeurIPS 2020 competitions using the Procgen Benchmark and MineRL to advance reinforcement learning sample efficiency and generalization.

1055

Image GPT

OpenAI's Image GPT (iGPT) demonstrates that a transformer model trained on pixel sequences can generate coherent images and learn competitive unsupervised visual features without domain-specific architectural priors.

1056

OpenAI API Release

OpenAI has released a general-purpose text-in, text-out API providing access to models from the GPT-3 family to enable the development of diverse AI-powered applications.

1057

OpenAI GPT-3: Language Models as Few-Shot Learners

OpenAI introduced GPT-3, a 175 billion parameter autoregressive language model that demonstrates strong few-shot performance across diverse NLP tasks without requiring task-specific fine-tuning.

1058

OpenAI AI and Efficiency Analysis

OpenAI's analysis reveals that algorithmic progress has reduced the compute needed to train neural networks to the same performance level by a factor of 2 every 16 months since 2012.

1059

OpenAI Jukebox

OpenAI Jukebox is a neural network capable of generating raw audio music, including rudimentary singing, conditioned on genre, artist, and lyrics.

1060

OpenAI: Improving Verifiability in AI Development

OpenAI and a multi-institutional coalition propose ten mechanisms to help stakeholders verify that AI systems adhere to stated ethics principles and safety standards to prevent competitive corner-cutting.

1061

OpenAI Microscope: A Tool for Neural Network Interpretability

OpenAI Microscope is a visualization tool that systematically visualizes every neuron in several common vision models to accelerate the research into reverse-engineering neural networks.

1062

OpenAI Standardizes on PyTorch

OpenAI has standardized its deep learning framework on PyTorch to increase research productivity and reduce iteration times for generative modeling.

1063

OpenAI Scaling Laws for Neural Language Models

OpenAI researchers discovered that language model performance follows predictable power-law relationships with model size, dataset size, and compute, enabling the optimal allocation of training resources.

1064

OpenAI Five: Dota 2 Reinforcement Learning

OpenAI Five is the first AI system to defeat world champions in an esports game, demonstrating that large-scale self-play reinforcement learning can achieve superhuman performance in complex, continuous state-action spaces.

1065

OpenAI Deep Double Descent

OpenAI researchers demonstrate that CNNs, ResNets, and transformers exhibit a double descent phenomenon where performance improves, degrades, and then improves again as model size, data size, or training time increases.

1066

OpenAI Procgen Benchmark Release

OpenAI has released the Procgen Benchmark, a suite of 16 procedurally-generated environments designed to measure reinforcement learning agents' ability to generalize skills to unseen levels.

1067

OpenAI Benchmarking Safe Exploration in Deep Reinforcement Learning

OpenAI introduces the Safety Gym benchmark suite to standardize constrained reinforcement learning as a primary formalism for safe exploration in high-dimensional continuous control environments.

1068

OpenAI Safety Gym Release

OpenAI has released Safety Gym, a suite of environments and tools designed to measure and improve the ability of reinforcement learning agents to respect safety constraints during training.

1069

GPT-2 1.5B release notes

OpenAI has released the largest version of GPT-2 with 1.5 billion parameters, completing its staged release process to provide a case study for responsible AI publication.

1070

OpenAI Solving Rubik's Cube with a Robot Hand

OpenAI has trained a pair of neural networks to solve a Rubik's Cube using a human-like robot hand, utilizing a new technique called Automatic Domain Randomization (ADR) to transfer simulation-learned skills to the physical world.

1071

OpenAI Scholars 2020 Program Applications

OpenAI has opened applications for its third OpenAI Scholars class, a four-month program providing stipends and mentorship to eight individuals from underrepresented groups to study deep learning.

1072

Fine-tuning GPT-2 from human preferences

OpenAI fine-tuned the 774M parameter GPT-2 model using human feedback to improve stylistic text continuation and summarization, discovering that human labelers often prefer accurate copying over novel synthesis.

1073

OpenAI Emergent Tool Use from Multi-Agent Interaction

OpenAI researchers discovered that agents playing a simulated game of hide-and-seek developed six distinct levels of complex tool use and strategies through multi-agent co-adaptation and self-play.

1074

OpenAI Testing Robustness Against Unforeseen Adversaries

OpenAI has introduced a new metric, Unforeseen Attack Robustness (UAR), to evaluate how well neural network classifiers defend against adversarial attacks not encountered during training.

1075

GPT-2 6-month follow-up

OpenAI released the 774M parameter GPT-2 model and shared lessons on the risks of synthetic text, the difficulty of detection, and the necessity of staged release strategies for large language models.

1076

OpenAI Learning Day: Cultivating Cross-Functional Expertise

OpenAI implements Learning Day, a weekly dedicated self-study day every Thursday to foster cross-functional technical growth and prevent professional stagnation.

1077

Microsoft and OpenAI Partnership for AGI Development

Microsoft invested $1 billion in OpenAI to co-develop a hardware and software platform on Azure designed to scale toward the creation of beneficial artificial general intelligence (AGI).

1078

OpenAI Framework for AI Safety Cooperation

OpenAI proposes four strategies to prevent collective action problems in AI development by promoting cooperation on safety norms and standards across the industry.

1079

OpenAI Robotics Symposium 2019

OpenAI hosted its first Robotics Symposium on April 27, 2019, bringing together experts from robotics and machine learning to discuss the development of robots that learn.

1080

OpenAI Scholars 2019 Final Projects

OpenAI released a summary of final projects from the 2019 OpenAI Scholars program, showcasing diverse applications of deep learning, reinforcement learning, and NLP across various domains.

1081

OpenAI Fellows Fall 2018 Final Projects

OpenAI announced the completion of its second class of OpenAI Fellows, with all six participants transitioning to full-time technical staff after a six-month apprenticeship.

1082

OpenAI Research: Transfer of Adversarial Robustness Between Perturbation Types

OpenAI researchers found that adversarial robustness does not consistently transfer between different perturbation types, suggesting that defenses must be evaluated against a diverse range of attack vectors to be truly effective.

1083

OpenAI MuseNet

MuseNet is a deep neural network capable of generating 4-minute musical compositions across 10 instruments by learning patterns of harmony, rhythm, and style from hundreds of thousands of MIDI files.

1084

OpenAI Sparse Transformer

OpenAI introduced the Sparse Transformer, a deep neural network that uses a reformulated attention mechanism to model sequences 30x longer than previous Transformers by reducing algorithmic complexity from O(N^2) to O(N sqrt(N)).

1085

OpenAI Five defeats Dota 2 world champions

OpenAI Five became the first AI to defeat esports world champions in a live livestreamed match, winning two back-to-back games against the Dota 2 team OG.

1086

OpenAI Five Finals Event Announcement

OpenAI announced a final live event for OpenAI Five on April 13, 2019, to demonstrate the competence, scalability, and human-AI interaction capabilities of its Dota 2 AI.

1087

Implicit Generation and Generalization Methods for Energy-Based Models

OpenAI researchers developed stable and scalable training methods for energy-based models (EBMs), achieving high-quality image generation and superior out-of-distribution generalization compared to likelihood-based models.

1088

OpenAI Scholars 2019 Program Participants

OpenAI introduced its 2019 cohort of Scholars, a multidisciplinary group of researchers focusing on reinforcement learning, natural language processing, and generative models.

1089

OpenAI LP: Transition to Capped-Profit Structure

OpenAI announced the creation of OpenAI LP, a hybrid capped-profit company designed to raise billions in capital for compute and talent while ensuring that the primary mission of safe AGI benefits all of humanity.

1090

OpenAI Activation Atlases

OpenAI and Google researchers introduced Activation Atlases, a visualization technique that maps interactions between neurons to reveal how neural networks represent concepts and identify decision-making weaknesses.

1091

Neural MMO: A Massively Multiagent Game Environment

OpenAI has released Neural MMO, a persistent, large-scale environment for reinforcement learning agents that demonstrates how increased population size and species diversity drive exploration and niche formation.

1092

OpenAI Spinning Up in Deep RL Workshop Review

OpenAI hosted a workshop based on its Spinning Up in Deep RL resource package to scale mentorship and skill development in reinforcement learning for a diverse group of participants.

1093

AI Safety Needs Social Scientists

OpenAI argues that long-term AI safety and alignment require the integration of social science to account for human cognitive biases and the complexities of human values.

1094

OpenAI GPT-2 Release and Implications

OpenAI introduced GPT-2, a 1.5 billion parameter unsupervised language model capable of zero-shot task performance and coherent text generation, while implementing a staged release strategy to mitigate misuse risks.

1095

OpenAI Research: Computational Limitations in Robust Classification

OpenAI researchers have identified classification tasks where robust classifiers exist but are computationally impossible to learn, linking the hardness of robust learning to the existence of cryptographic primitives.

1096

OpenAI Summer Fellows 2018 Final Projects

OpenAI's 2018 Summer Fellows completed research projects focusing on reinforcement learning generalization, distributed training patterns, and generative model expressivity.

1097

How AI Training Scales: Predicting Parallelizability via Gradient Noise Scale

OpenAI discovered that the gradient noise scale, a statistical metric quantifying the signal-to-noise ratio of network gradients, can predict the maximum useful batch size for neural network training across diverse tasks.

1098

Quantifying Generalization in Reinforcement Learning

OpenAI introduces CoinRun, a procedurally generated training environment designed to quantify and improve an agent's ability to transfer learned skills to novel situations in reinforcement learning.

1099

OpenAI Spinning Up in Deep RL

OpenAI has released Spinning Up in Deep RL, an educational resource providing code, tutorials, and documentation to help practitioners master deep reinforcement learning.

1100

OpenAI Learning Concepts with Energy Functions

OpenAI has developed a technique using energy functions to enable agents to learn and extract concepts that can be transferred across dissimilar environments without retraining.