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.
Consortium Aims to Standardize Chiplet Interconnect
Mark Nossokoff, Bob Sorensen
Seeking to establish a die-to-die interconnect standard and foster an open chiplet ecosystem, a strong collection of major chip makers and users recently announced the formation of the UCIe (Universal Chiplet Interconnect Express) industry consortium. The consortium has published version 1.0 of the UCIe specification, covering the die-to-die I/O physical layer, die-to-die protocols, and software stack. Promoter members of the consortium are Advanced Semiconductor Engineering, Inc. (ASE), AMD, Arm, Google Cloud, Intel Corporation, Meta, Microsoft Corporation, Qualcomm Incorporated, Samsung, and Taiwan Semiconductor Manufacturing Company (TSMC)
3 202022 | HYP_Link
Deep Transfer Learning Framework Applied to Radiation Therapy
Alex Norton, Tom Sorensen
At the conclusion of 2021, researchers at UNC Charlotte and Duke University Medical Center published results of work done to use transfer learning methods to generate fluence maps for radiation therapy, aimed at providing medical professionals with more capability and information in fighting adrenal cancers. The technique uses a deep transfer learning model trained on a much larger dataset that can be applied to a smaller data set for a specific application.
The initial model was trained on pancreas treatment plans, then retuned and applied to a smaller set of data points on adrenal cancers. The output of the model generates a fluence map for specific IMRT beam-based treatments for adrenal cancers.
According to the researchers, this approach is meant to supplement but not replace human expertise in the field and is reliant on human expertise to finetune and improve the AI model.