Introducing artificial intelligence
AI programmers should pilot prejudice and security bounties for AI methods to reinforce incentives and procedures for broad-based evaluation of AI systems.Sharing of AI events. AI programmers should share more info about AI events, such as through collaborative stations.
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Software Mechanisms and TipsAudit trails. Standard setting bodies must operate with academia and business to develop audit management requirements for safety-critical programs of AI systems.Interpretability. Organizations growing AI and financing bodies need to encourage research to the interpretability of AI systems, with a concentration on supporting hazard assessment and auditing.AI programmers should develop, discuss, and utilize suites of resources such as privacy-preserving machine learning which include measures of performance against standards that are common.
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Hardware Mechanisms and Tips
Industry and academia must work together to create hardware safety attributes for AI accelerators or establish best practices for using secure hardware (like protected enclaves on commodity hardware) in machine learning contexts.High-precision compute dimension. One or more AI labs ought to gauge the calculating power involved in one project in fantastic detail and document on lessons learned concerning the possibility of broader adoption of these procedures.Government funding bodies need to substantially increase funds for calculating electricity resources for investigators in academia, so as to enhance the ability of these researchers to confirm claims made by business.