Japan Stands Up Quantum Technology Industry Group to Boost Commercial Quantum Prospects

A recent United Kingdom policy paper entitled Establishing a pro-innovation approach to regulating AI identifies unique AI-related regulatory challenges, outlines six cross-sectoral potential solutions, and provides a future outlook on bringing these recommendations to reality. The document contributes to the increasingly unique and regionally divergent approach to AI regulation the UK is adopting as part of the greater framework seen in the UK national AI strategy plan published in September 2021. This latest publication, presented to Parliament by the Secretary of State for Digital, Culture, Media and Sport, prominently espouses innovation-forward policy making, while acknowledging and addressing the inherent risks of developing and deploying AI technology. For UK planners, a system of voluntary, regulatory, and quasi-regulatory policies enables greater UK government responsiveness to technological, political, or contextual developments through an evolving cohort of regulatory bodies, all aiding responsible use of AI in respective fields and through different lenses.

MLCommons, an international artificial intelligence (AI) standards body formed in 2018, launched MLPerf Tiny, their first benchmark targeted at the inference capabilities of edge and embedded devices, or what they call "intelligence in everyday devices". The new benchmark is now part of the overall MLPerf benchmark suite, which measures AI training and inference performance on a wide variety of workloads, including natural language processing and image recognition. The benchmark covers four machine learning (ML) tasks focused on camera and microphone sensors as inputs: keyword spotting, visual wake words, tiny image classification, and anomaly detection. Some important use cases include smart home security, virtual assistants, and predictive maintenance.