Exploring institutions for global AI governance

Exploring institutions for global AI governance

New white paper investigates models and functions of international institutions that could help manage opportunities and mitigate risks of advanced AI. Growing awareness of the global impact of advanced artificial intelligence (AI) has inspired public discussions about the need for international governance structures to help manage opportunities and mitigate risks involved. Many discussions have drawn on analogies with the ICAO (International Civil Aviation Organization) in civil aviation; CERN (European Organization for Nuclear Research) in particle physics; IAEA (International Atomic Energy Agency) in nuclear technology, and intergovernmental and multi-stakeholder organisations in many other domains. And yet, while analogies can be a useful start, the technologies emerging from AI will be unlike aviation, particle physics, or nuclear technology. To succeed with AI governance, we need to better understand: what specific benefits and risks we need to manage internationally, what governance functions those benefits and risks require, what organisations can best provide those functions.Read More

RoboCat: A self-improving robotic agent

Robots are quickly becoming part of our everyday lives, but they’re often only programmed to perform specific tasks well. While harnessing recent advances in AI could lead to robots that could help in many more ways, progress in building general-purpose robots is slower in part because of the time needed to collect real-world training data. Our latest paper introduces a self-improving AI agent for robotics, RoboCat, that learns to perform a variety of tasks across different arms, and then self-generates new training data to improve its technique.Read More

RoboCat: A self-improving robotic agent

RoboCat: A self-improving robotic agent

Robots are quickly becoming part of our everyday lives, but they’re often only programmed to perform specific tasks well. While harnessing recent advances in AI could lead to robots that could help in many more ways, progress in building general-purpose robots is slower in part because of the time needed to collect real-world training data. Our latest paper introduces a self-improving AI agent for robotics, RoboCat, that learns to perform a variety of tasks across different arms, and then self-generates new training data to improve its technique.Read More