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.
AI Powered Verusen and Machine Compare Partnership Targets Supply Chain Pain Points
Tom Sorensen, Alex Norton
In an announcement made in December, 2021, Verusen, a startup specializing in leveraging AI resources to support global supply chains, detailed a recently formed partnership with Machine Compare, a supplier of one of the world's largest databases for machinery and leading B2B marketplace for buyers and sellers of industrial spare parts. The partnership is aimed at enhancing the customer experience, limiting risk, reducing waste, and helping companies conduct materials management and commerce in a new and efficient way. Verusen founder and CEO Paul Noble explains the partnership is targeted to resolve a painful and wasteful process and will ultimately allow manufacturers to realize a whole new level of sustainability. For his part, Machine Compare CEO Ben Findlay is looking for a reduction in downtime, stockouts, and costs. Furthermore, the burdens lifted by the Verusen AI capabilities are targeted to reduce the amount of manpower committed to time-consuming, reactive tasks, allowing for a more proactive and long-term management of goals.
1 202022 | HYP_Link
US Government Consortium Launches Quantum Network Research Project
The US government recently stood up a consortium of six Washington D.C.-based federal agencies to explore a range of quantum technologies necessary to create, demonstrate, and operate DC-QNet, a regional, multi-kilometer quantum network testbed. The six participating agencies span a range of US government mission agencies including the National Security Agency, the US Naval Research Laboratory, the National Institute of Standards and Technology, and the National Aeronautics and Space Administration. The program targets key underlying technologies needed to implement a metroarea quantum network that includes high-fidelity quantum memory, single photon devices, and related network metrology as well as mechanisms to support quantum entanglement between network nodes in a quantum computer. Details about project schedule and budget have not yet been made available, but each participating agency will be responsible for funding its research activities.