{"id":91361,"date":"2024-04-12T09:50:26","date_gmt":"2024-04-12T13:50:26","guid":{"rendered":"https:\/\/danielschristian.com\/learning-ecosystems\/?p=91361"},"modified":"2024-04-12T09:50:26","modified_gmt":"2024-04-12T13:50:26","slug":"ai-for-the-physical-world-kahn","status":"publish","type":"post","link":"http:\/\/danielschristian.com\/learning-ecosystems\/2024\/04\/12\/ai-for-the-physical-world-kahn\/","title":{"rendered":"AI for the physical world [Kahn]"},"content":{"rendered":"<p><a href=\"https:\/\/www.superhuman.ai\/p\/ai-can-read-realworld-sensors\" target=\"_blank\" rel=\"noopener\"><strong>AI for the physical world<\/strong><\/a> &#8212; from superhuman.ai by Zain Kahn<\/p>\n<p>Excerpt: <em><span style=\"color: #800000;\">(emphasis DSC)<\/span><\/em><\/p>\n<p>A new company called Archetype is trying to tackle that problem: <span style=\"color: #800000;\"><strong>It wants to make AI useful for more than just interacting with and understanding the digital realm. The startup just unveiled Newton \u2014 \u201cthe first foundation model that understands the physical world.\u201d<\/strong><\/span><\/p>\n<p><strong>What\u2019s it for?<\/strong><br \/>\nA warehouse or factory might have 100 different sensors that have to be analyzed separately to figure out whether the entire system is working as intended. Newton can understand and interpret all of the sensors at the same time, giving a better overview of how everything\u2019s working together. Another benefit: You can ask Newton questions in plain English without needing much technical expertise.<\/p>\n<p><strong>How does it work?<\/strong><\/p>\n<ul>\n<li>Newton collects data from radar, motion sensors, and chemical and environmental trackers<\/li>\n<li>It uses an LLM to combine each of those data streams into a cohesive package<\/li>\n<li>It translates that data into text, visualizations, or code so it\u2019s easy to understand<\/li>\n<\/ul>\n<hr \/>\n<p><a href=\"https:\/\/venturebeat.com\/ai\/apples-25-50-million-shutterstock-deal-highlights-fierce-competition-for-ai-training-data\/\" target=\"_blank\" rel=\"noopener\"><strong>Apple\u2019s $25-50 million Shutterstock deal highlights fierce competition for AI training data<\/strong><\/a> &#8212; from venturebeat.com by Michael Nu\u00f1ez; via Tom Barrett&#8217;s Prompcraft e-newsletter<\/p>\n<p style=\"padding-left: 40px;\">Apple\u00a0has entered into a significant agreement with stock photography provider\u00a0Shutterstock\u00a0to license millions of images for training its artificial intelligence models. According to a Reuters report, the deal is estimated to be worth\u00a0between $25 million and $50 million, placing Apple among several tech giants racing to secure vast troves of data to power their AI systems.<\/p>\n<hr \/>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>AI for the physical world &#8212; from superhuman.ai by Zain Kahn Excerpt: (emphasis DSC) A new company called Archetype is trying to tackle that problem: It wants to make AI useful for more than just interacting with and understanding the digital realm. The startup just unveiled Newton \u2014 \u201cthe first foundation model that understands the [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[113,329,139,356,112,298,252,144,309],"tags":[],"class_list":["post-91361","post","type-post","status-publish","format-standard","hentry","category-21st-century","category-24x7x365-access","category-apple","category-artificial-intelligence-agents-llms-and-related","category-corporate-business-world","category-data-related-items","category-digital-photography","category-photography","category-platforms"],"_links":{"self":[{"href":"http:\/\/danielschristian.com\/learning-ecosystems\/wp-json\/wp\/v2\/posts\/91361","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/danielschristian.com\/learning-ecosystems\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/danielschristian.com\/learning-ecosystems\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/danielschristian.com\/learning-ecosystems\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/danielschristian.com\/learning-ecosystems\/wp-json\/wp\/v2\/comments?post=91361"}],"version-history":[{"count":5,"href":"http:\/\/danielschristian.com\/learning-ecosystems\/wp-json\/wp\/v2\/posts\/91361\/revisions"}],"predecessor-version":[{"id":91381,"href":"http:\/\/danielschristian.com\/learning-ecosystems\/wp-json\/wp\/v2\/posts\/91361\/revisions\/91381"}],"wp:attachment":[{"href":"http:\/\/danielschristian.com\/learning-ecosystems\/wp-json\/wp\/v2\/media?parent=91361"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/danielschristian.com\/learning-ecosystems\/wp-json\/wp\/v2\/categories?post=91361"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/danielschristian.com\/learning-ecosystems\/wp-json\/wp\/v2\/tags?post=91361"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}