In part 2 of our not-to-be-missed @HPCpodcast with Argonne National Laboratory Associate Director Rick Stevens, he discusses some of the important advances that had, by 2015, likely ended the cycle of AI for science winters. He also delves into the major challenges in AI for science, such as building models that are transparent and unbiased while also robust and secure. And Stevens looks at important upcoming AI for science breakthrough use cases, including the welcome news – for researchers beset by mountains of scientific papers – of utilizing large natural language modeling to ingest and collate existing knowledge of a scientific problem, enabling analysis of the literature that, Stevens said, goes well beyond a Google search….
@HPCpodcast: Argonne’s Rick Stevens on AI for Science (Part 2) – Coming Breakthroughs, Ethics and the Replacement of Scientists by Robots
March 11, 2022 by
Cortical.io Taps FPGAs for Message Intelligence
April 28, 2020 by
Today Cortical.io announced Message Intelligence, an out-of-the-box solution that filters, classifies, and routes streams of messages in real time at a massive scale. “Combining the Cortical.io proven Semantic Folding method with FPGA-powered acceleration from Xilinx, Cortical.io Message Intelligence is a purpose-built appliance, easily installed and configured. It provides human-level accuracy in filtering and classifying messages, enabling businesses to more quickly process and respond to messages of any kind and also mitigate legal risks.”
