AI capabilities are evolving quickly and pose novel—and likely significant—risks. In these rapidly changing conditions, how can policymakers effectively anticipate and manage risks from the most advanced and capable AI systems at the frontier of the field? This Roundtable Report summarizes some of the key themes and conclusions of a July 2023 workshop on this topic jointly hosted by CSET and Google DeepMind.

This explainer overviews techniques to produce smaller and more efficient language models that require fewer resources to develop and operate. Importantly, information on how to leverage these techniques, and many of the subsequent small models, are openly available online for anyone to use. The combination of both small (i.e., easy to use) and open (i.e., easy to access) could have significant implications for artificial intelligence development.

Artificial intelligence that makes news headlines, such as ChatGPT, typically runs in well-maintained data centers with an abundant supply of compute and power. However, these resources are more limited on many systems in the real world, such as drones, satellites, or ground vehicles. As a result, the AI that can run onboard these devices will often be inferior to state of the art models. That can affect their usability and the need for additional safeguards in high-risk contexts. This issue brief contextualizes these challenges and provides policymakers with recommendations on how to engage with these technologies.

Foundation models could eventually introduce several pathways for undermining state security: accidents, inadvertent escalation, unintentional conflict, the proliferation of weapons, and the interference with human diplomacy are just a few on a long list. The Confidence-Building Measures for Artificial Intelligence workshop hosted by the Geopolitics Team at OpenAI and the Berkeley Risk and Security Lab at the University of California brought together a multistakeholder group to think through the tools and strategies to mitigate the potential risks introduced by foundation models to international security.

Jenny Jun's testimony before the House Foreign Affairs Subcommittee on Indo-Pacific for a hearing titled, "Illicit IT: Bankrolling Kim Jong Un."

The current AI-for-cybersecurity paradigm focuses on detection using automated tools, but it has largely neglected holistic autonomous cyber defense systems — ones that can act without human tasking. That is poised to change as tools are proliferating for training reinforcement learning-based AI agents to provide broader autonomous cybersecurity capabilities. The resulting agents are still rudimentary and publications are few, but the current barriers are surmountable and effective agents would be a substantial boon to society.

Progress in artificial intelligence (AI) depends on talented researchers, well-designed algorithms, quality datasets, and powerful hardware. The relative importance of these factors is often debated, with many recent “notable” models requiring massive expenditures of advanced hardware. But how important is computational power for AI progress in general? This data brief explores the results of a survey of more than 400 AI researchers to evaluate the importance and distribution of computational needs.

Artificial intelligence systems are rapidly being deployed in all sectors of the economy, yet significant research has demonstrated that these systems can be vulnerable to a wide array of attacks. How different are these problems from more common cybersecurity vulnerabilities? What legal ambiguities do they create, and how can organizations ameliorate them? This report, produced in collaboration with the Program on Geopolitics, Technology, and Governance at the Stanford Cyber Policy Center, presents the recommendations of a July 2022 workshop of experts to help answer these questions.

Militaries seek to harness artificial intelligence for decision advantage. Yet AI systems introduce a new source of uncertainty in the likelihood of technical failures. Such failures could interact with strategic and human factors in ways that lead to miscalculation and escalation in a crisis or conflict. Harnessing AI effectively requires managing these risk trade-offs by reducing the likelihood, and containing the consequences of, AI failures.

Despite being a small city-state, Singapore’s star continues to rise as an artificial intelligence hub presenting significant opportunities for international collaboration. Initiatives such as fast-tracking patent approval, incentivizing private investment, and addressing talent shortfalls are making the country a rapidly growing global AI hub. Such initiatives offer potential models for those seeking to leverage the technology and opportunities for collaboration in AI education and talent exchanges, research and development, and governance. The United States and Singapore share similar goals regarding the development and use of trusted and responsible AI and should continue to foster greater collaboration among public and private sector entities.