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Introducing next-generation audio models in the API
For the first time, developers can also instruct the text-to-speech model to speak in a specific way—for example, “talk like a sympathetic customer service agent”—unlocking a new level of customization for voice agents.
OpenAI News
New tools for building agents
Today, we’re releasing the first set of building blocks that will help developers and enterprises build useful and reliable agents. We view agents as systems that independently accomplish tasks on behalf of users. Over the past year, we’ve introduced new...
OpenAI News
MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineering
We introduce MLE-bench, a benchmark for measuring how well AI agents perform at machine learning engineering.
OpenAI News
MavenAGI launches automated customer support agents powered by OpenAI
MavenAGI is a new software company for the AI era. They recently launched an AI customer service agent, built on the flexibility of GPT-4, which a number of companies like Tripadvisor, Clickup and Rho are already using to save time and better serve their...
OpenAI News
Klarna's AI assistant does the work of 700 full-time agents
Klarna is using AI to revolutionize personal shopping, customer service, and employee productivity.
OpenAI News
Practices for Governing Agentic AI Systems
Agentic AI systems—AI systems that can pursue complex goals with limited direct supervision—are likely to be broadly useful if we can integrate them responsibly into our society. While such systems have substantial potential to help people more efficiently...
OpenAI News
Learning to play Minecraft with Video PreTraining
We trained a neural network to play Minecraft by Video PreTraining (VPT) on a massive unlabeled video dataset of human Minecraft play, while using only a small amount of labeled contractor data. With fine-tuning, our model can learn to craft diamond tools,...
OpenAI News
Safety Gym
We’re releasing Safety Gym, a suite of environments and tools for measuring progress towards reinforcement learning agents that respect safety constraints while training.
OpenAI News
Benchmarking safe exploration in deep reinforcement learning
Reinforcement learning (RL) agents need to explore their environments in order to learn optimal policies by trial and error. In many environments, safety is a critical concern and certain errors are unacceptable: for example, robotics systems that interact...
OpenAI News
Emergent tool use from multi-agent interaction
We’ve observed agents discovering progressively more complex tool use while playing a simple game of hide-and-seek. Through training in our new simulated hide-and-seek environment, agents build a series of six distinct strategies and counterstrategies, some...
OpenAI News
Neural MMO: A massively multiagent game environment
We’re releasing a Neural MMO, a massively multiagent game environment for reinforcement learning agents. Our platform supports a large, variable number of agents within a persistent and open-ended task. The inclusion of many agents and species leads to...