AI Policy @🤗: Response to the U.S. NTIA's Request for Comment on AI Accountability
AI Policy @🤗: Response to the U.S. NTIA's Request for Comment on AI Accountability.
235 AI news items in policy & safety, newest first — page 8 of 8.
AI Policy @🤗: Response to the U.S. NTIA's Request for Comment on AI Accountability.
Announcing our new Content Guidelines and Policy.
Now is a good time to start thinking about the governance of superintelligence—future AI systems dramatically more capable than even AGI.
Creating Privacy Preserving AI with Substra.
Ensuring that AI systems are built, deployed, and used safely is critical to our mission.
We’ve implemented initial support for plugins in ChatGPT. Plugins are tools designed specifically for language models with safety as a core principle, and help ChatGPT access up-to-date...
New users can start creating straight away. Lessons learned from deployment and improvements to our safety systems make wider availability possible.
Proximal Policy Optimization (PPO).
We are implementing a new technique so that DALL·E generates images of people that more accurately reflect the diversity of the world’s population.
Early users have created over 3 million images to date and helped us improve our safety processes. We’re excited to begin adding up to 1,000 new users from our waitlist each week.
We describe our latest thinking in the hope of helping other AI developers address safety and misuse of deployed models.
Wider availability made possible by safety progress.
OpenAI is committed to developing general-purpose artificial intelligence that benefits all humanity, and we believe that achieving our goal requires expertise in public policy as well as...
We’re releasing Safety Gym, a suite of environments and tools for measuring progress towards reinforcement learning agents that respect safety constraints while training.
We’ve written a policy research paper identifying four strategies that can be used today to improve the likelihood of long-term industry cooperation on safety norms in AI: communicating risks and...
We’ve written a paper arguing that long-term AI safety research needs social scientists to ensure AI alignment algorithms succeed when actual humans are involved. Properly aligning advanced AI...
We’re proposing an AI safety technique called iterated amplification that lets us specify complicated behaviors and goals that are beyond human scale, by demonstrating how to decompose a task into...
Learning policy representations in multiagent systems.
We’re proposing an AI safety technique which trains agents to debate topics with one another, using a human to judge who wins.
We’re releasing an experimental metalearning approach called Evolved Policy Gradients, a method that evolves the loss function of learning agents, which can enable fast training on novel tasks. ...
Variance reduction for policy gradient with action-dependent factorized baselines.
We’re releasing a new class of reinforcement learning algorithms, Proximal Policy Optimization (PPO), which perform comparably or better than state-of-the-art approaches while being much simpler...
Equivalence between policy gradients and soft Q-learning.
We (along with researchers from Berkeley and Stanford) are co-authors on today’s paper led by Google Brain researchers, Concrete Problems in AI Safety. The paper explores many research problems...