
AI Powered Verusen and Machine Compare Partnership Targets Supply Chain Pain Points
$1,500.00
Authors: Tom Sorensen, Alex Norton
Publication Date: 1 202022
Length: 1 pages
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.
Related Products
Barcelona Supercomputing Center Trains Spanish NLP Model
Alex Norton, Bob Sorensen
Recently, the Barcelona Supercomputing Center (BSC) trained the first large artificial intelligence (AI) model designed to understand, speak, and write in the Spanish language. The system, named MarIA, was trained on the MareNostrum supercomputer at the BSC, leveraging 59 TBs of language data from the Biblioteca Nacional de España, one of the world's largest public libraries. The model is said to be an expert in both writing and understanding the Spanish language and is free to use by any developer, company, or entity. The system has a wide variety of potential applications including summarization applications, chatbots, smart searches, translation engines, and automatic subtitling chatbots.
9 202021 | HYP_Link
New Error Correction Scheme Seeks to Advance Quantum Computing Capabilities
Bob Sorensen, Tom Sorensen
Researchers at the US-based Lawrence Berkeley National Lab (LBNL) recently reported a new approach to error mitigation in a quantum computer (QC) that targets error-producing noise, a ubiquitous problem that can severely limit the performance and utility of existing and near-future quantum computers. The method developed at LBNL consists of taking an initial noisy target circuit and constructing an analogous estimation circuit that is configured specifically for accurate noise characterization. The information gathered from running the estimation circuit is then applied to correct the noise in the original target circuit.
3 202022 | HYP_Link