The video of the Second CITAC Web Seminar: Award Ceremony of the CITAC Best Paper 2021, held on 2022/06/21, is available on the CITAC Youtube Channel: #CITAC# #metrology# #comparability# #qualityassurance# #analyticalchemistry# #measurementuncertainty#

The Second CITAC Web-seminar was held on 7 May 2021, focused on presenting the CITAC Best Papers 2020. About 40 people participated in the online event whose video is available on youtube: #CITAC# #analyticalchemistry# #metrology# #chemistry# #comparability#

The 36th CITAC Member's Meeting took place online on 3rd May 2021. The members discussed the current and future activities of CITAC. # CITAC #analyticalchemistry# #metrology# #chemistry# #comparability#

The second CITAC web-seminar dedicated to presenting the three papers awarded as Best CITAC papers 2020 will be, free of charge, on Friday 07 May 2021 (13:00 to 15:00 CET). #CITAC# #metrology# #comparability# #analyticalchemistry# #accreditation#

Dr Robert Kaarls was awarded the CITAC Honorary Membership for his exceptional individual contributions to CITAC and to the promotion of metrological traceability in chemical measurements. #CITAC# #metrology# #chemistry# #traceability# #comparability# #quality# #analyticalchemistry#

Artificial Analysis is proud to support the Korean Government as an official evaluation partner for the Sovereign AI Model Foundation project. We signed an MOU to formalize this role as an independent evaluator supporting Korea’s efforts to develop globally competitive frontier AI models and strengthen its domestic AI ecosystem In the most recent round of the Korean Sovereign AI competition hosted by National IT Industry Promotion Agency (NIPA), models from participating labs were benchmarked using the Artificial Analysis Intelligence Index, which accounted for 25% of the overall judging criteria. Artificial Analysis brings an independent global benchmark to the program, strengthening the rigor, credibility, and comparability of the evaluation. This partnership reflects our broader mission to advance independent evaluation of AI capabilities and support sovereign AI initiatives worldwide.

Ever spend way too long hunting across scattered sources just to find "which benchmark actually fits this task"? Title: Benchmark Radar: A Living Database and Search Engine for AI Benchmarks and Evaluation URL: ❓ What is Benchmark Radar? 💡 It's a search engine that crawls 37 sources daily, arXiv, Hugging Face, GitHub and more, and pulls each benchmark's paper, dataset, repository, and score history into one place, with a CLI for offline queries. ❓ How much data are we talking about? 💡 1,283 source records from four catalogs, covering 12,916 numeric scores across 790 benchmarks. Daily collection alone logs 11,068 observations across 6,546 distinct artifacts. ❓ Why can't scores just be compared directly? 💡 Only 82 of the scored records use a verified 0-100 percentage scale, the rest use different or unverified scales, and roughly half lack a known release date, so naive comparisons would be misleading. ❓ So what's the fix? 💡 Instead of forcing comparability, it preserves source identity and citations so readers can inspect the evaluation conditions themselves. #LLMEval# #Benchmarks#

Composability and institutional control don't have to be trade-offs. Canton was designed to give participants both, so an asset can guarantee title, protect investors, and remain fully composable with the rest of the network. @Wesarn_real with @Santiag78758327 on @RealVision.

Postgres compatibility is a start. Production is the real test. For AI apps, that means scale, availability, recovery, security and fewer brittle pipelines between transactional and analytical data. Check out this breakdown on Snowflake Postgres for teams building beyond the demo: