{"id":1190,"date":"2024-10-31T23:51:23","date_gmt":"2024-10-31T23:51:23","guid":{"rendered":"https:\/\/wordpress.cels.anl.gov\/sc24\/?p=1190"},"modified":"2024-11-01T20:00:27","modified_gmt":"2024-11-01T20:00:27","slug":"generative-ai-models-for-science","status":"publish","type":"post","link":"https:\/\/wordpress.cels.anl.gov\/sc24\/generative-ai-models-for-science\/","title":{"rendered":"Generative AI Models for Science"},"content":{"rendered":"\n

Building and training large language models (LLMs) for scientific discovery pose significant technical challenges, often beyond the resources of most organizations. Multi-institutional collaborations are essential to ensuring progress. Led by Argonne, the international Trillion Parameter Consortium has organized a half-day workshop to accelerate the development and use of generative AI<\/a> for science and engineering. Speakers include Argonne\u2019s Rick Stevens, Valerie Taylor and Charlie Catlett with colleagues from Canada, Japan and Spain.<\/p>\n\n\n\n

In another session, researchers Hongwei Jin and Krishnan Raghavan are exploring how LLMs can automatically identify unusual activity in computing processes<\/a>, making systems more reliable and secure. And Anshu Dubey will co-lead an interactive Birds of a Feather session on the use of foundational LLM technologies for different high performance computing targets<\/a>.\u00a0<\/p>\n","protected":false},"excerpt":{"rendered":"

Building and training large language models (LLMs) for scientific discovery pose significant technical challenges, often beyond the resources of most organizations. Multi-institutional collaborations are essential to ensuring progress. Led by Argonne, the international Trillion Parameter Consortium has organized a half-day workshop to accelerate the development and use of generative AI for science and engineering. Speakers <\/p>\n","protected":false},"author":1423,"featured_media":1191,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[3],"tags":[],"class_list":["post-1190","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-featured-activities"],"acf":[],"_links":{"self":[{"href":"https:\/\/wordpress.cels.anl.gov\/sc24\/wp-json\/wp\/v2\/posts\/1190","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/wordpress.cels.anl.gov\/sc24\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/wordpress.cels.anl.gov\/sc24\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/wordpress.cels.anl.gov\/sc24\/wp-json\/wp\/v2\/users\/1423"}],"replies":[{"embeddable":true,"href":"https:\/\/wordpress.cels.anl.gov\/sc24\/wp-json\/wp\/v2\/comments?post=1190"}],"version-history":[{"count":1,"href":"https:\/\/wordpress.cels.anl.gov\/sc24\/wp-json\/wp\/v2\/posts\/1190\/revisions"}],"predecessor-version":[{"id":1192,"href":"https:\/\/wordpress.cels.anl.gov\/sc24\/wp-json\/wp\/v2\/posts\/1190\/revisions\/1192"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/wordpress.cels.anl.gov\/sc24\/wp-json\/wp\/v2\/media\/1191"}],"wp:attachment":[{"href":"https:\/\/wordpress.cels.anl.gov\/sc24\/wp-json\/wp\/v2\/media?parent=1190"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/wordpress.cels.anl.gov\/sc24\/wp-json\/wp\/v2\/categories?post=1190"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/wordpress.cels.anl.gov\/sc24\/wp-json\/wp\/v2\/tags?post=1190"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}