Coding tools, APIs, and developer resources
Open source has become essential for driving innovation in robotics and autonomy. By providing access to critical infrastructure — from simulation frameworks to AI models — NVIDIA is enabling collaborative development that accelerates the path to safer, more capable autonomous systems.
AI has taken center stage in financial services, automating the research and execution behind algorithmic trading and helping banks more accurately detect fraud and money laundering — all while improving risk management practices and expediting document processing. The sixth annual “NVIDIA State of AI in Financial Services” report, based on a survey of more than Read Article
Developers can now submit apps for review and publication in ChatGPT, with approved apps appearing in a new in-product directory for easy discovery. Updated tools, guidelines, and the Apps SDK help developers build powerful chat-native experiences that bring real-world actions into ChatGPT.
The new ChatGPT Images is powered by our flagship image generation model, delivering more precise edits, consistent details, and image generation up to 4× faster. The upgraded model is rolling out to all ChatGPT users today and is also available in the API as GPT-Image-1.5.
OpenAI Codex is now generally available with powerful new features for developers: a Slack integration, Codex SDK, and admin tools like usage dashboards and workspace management—making Codex easier to use and manage at scale.
Our latest video generation model is more physically accurate, realistic, and controllable than prior systems. It also features synchronized dialogue and sound effects. Create with it in the new Sora app.
At OpenAI, we build tools to help people solve hard problems—including nonprofits working on the frontlines of their communities. The OpenAI Academy is teaming up with the Walton Family Foundation, Emerson Collective, and a network of local nonprofit organizations to host the Nonprofit Jam—a one-day, nationwide event bringing together more than 1,000 nonprofit leaders across 10 locations.
OpenAI’s comment to the NTIA on open model weights This comment was submitted by OpenAI in response to NTIA’s March 2024 Request for Information on Dual-Use Foundation Models with Widely Available Weights.
We funded 10 teams from around the world to design ideas and tools to collectively govern AI. We summarize the innovations, outline our learnings, and call for researchers and engineers to join us as we continue this work.
Microsoft is investing $1 billion in OpenAI to support us building artificial general intelligence (AGI) with widely distributed economic benefits. We’re partnering to develop a hardware and software platform within Microsoft Azure which will scale to AGI. We’ll jointly develop new Azure AI supercomputing technologies, and Microsoft will become our exclusive cloud provider—so we’ll be working hard together to further extend Microsoft Azure’s capabilities in large-scale AI systems.
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 better exploration, divergent niche formation, and greater overall competence.
We are now accepting applications for our second cohort of OpenAI Scholars, a program where we provide 6–10 stipends and mentorship to individuals from underrepresented groups to study deep learning full-time for 3 months and open-source a project.
We’ve developed a hierarchical reinforcement learning algorithm that learns high-level actions useful for solving a range of tasks, allowing fast solving of tasks requiring thousands of timesteps. Our algorithm, when applied to a set of navigation problems, discovers a set of high-level actions for walking and crawling in different directions, which enables the agent to master new navigation tasks quickly.
We’re releasing an algorithm which accounts for the fact that other agents are learning too, and discovers self-interested yet collaborative strategies like tit-for-tat in the iterated prisoner’s dilemma. This algorithm, Learning with Opponent-Learning Awareness (LOLA), is a small step towards agents that model other minds.