Report from the AI Race Avoidance Workshop

2017-12-04 · Source: AI Roadmap Institute Blog - Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Public Policy & Governance · Depth: Intermediate, extended

Summary

GoodAI and the AI Roadmap Institute Tokyo hosted an "AI Race Avoidance Workshop" on October 13, 2017, bringing together diverse stakeholders to discuss the potential pitfalls of a race for transformative AI. The workshop addressed concerns that such a race could lead to neglected safety procedures, unshared benefits, and a "winner takes all" scenario. Participants explored ways to understand and manage AI race dynamics, identify key actors, and incentivize cooperation. The General AI Challenge Round 2: Race Avoidance, launching on January 18, 2018, aims to crowdsource mitigation strategies. Key discussions included understanding race frameworks, assessing whether competition is inherently negative, identifying stakeholder roles, and analyzing incentives for cooperation. The report also considered various future scenarios, from a single dominant actor to co-evolutionary development, and emphasized the importance of transparency and trust.

Key takeaway

For AI Scientists and Research Scientists developing advanced AI, you should actively engage in cross-disciplinary discussions and transparently share your roadmaps and motivations. This approach helps build trust and identify robust mechanisms for cooperation, which is crucial for mitigating the risks of an AI race and ensuring that the benefits of transformative AI are broadly shared, rather than concentrated among a few.

Key insights

Mitigating an AI race requires understanding its dynamics, incentivizing cooperation, and fostering global, transparent dialogue.

Principles

• People pose a greater risk than AI itself.
• Transparency and predictability build trust among stakeholders.
• AI race mitigation is an "insurance" against unhappy futures.

Method

The workshop utilized frameworks, historical meta-patterns, first-principle thinking, and simulation games (e.g., "Superintelligence mod" for Civilization 5) to better understand AI race dynamics and foster interdisciplinary discussion.

In practice

• Study meta-patterns from historical races.
• Develop simulation games to understand race problems.
• Promote AI safety research beyond small communities.

Topics

• AI Race Dynamics
• AI Safety
• Artificial General Intelligence
• Stakeholder Cooperation
• General AI Challenge

Best for: AI Scientist, Research Scientist, AI Researcher, AI Ethicist, Policy Maker

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Editorial summary, takeaway, and curation by AIssential. Original article published by AI Roadmap Institute Blog - Medium.