{"id":5272,"date":"2024-02-02T08:15:25","date_gmt":"2024-02-02T01:15:25","guid":{"rendered":"https:\/\/wam.vn\/en\/?p=5272"},"modified":"2024-02-02T08:15:25","modified_gmt":"2024-02-02T01:15:25","slug":"nvidias-new-ai-agent-will-change-the-world","status":"publish","type":"post","link":"https:\/\/wam.vn\/en\/nvidias-new-ai-agent-will-change-the-world\/","title":{"rendered":"Nvidias NEW &#8220;AI AGENT&#8221; Will Change The WORLD!"},"content":{"rendered":"<p><em><strong>TLDR<\/strong>: Nvidia&#8217;s Jim Fan unveils the Foundation Agent, bridging virtual and physical worlds for applications in gaming, metaverse, and robotics, demonstrating AI&#8217;s evolving versatility.<\/em><\/p>\n<p>This article is a summary of a You Tube video &#8220;Nvidias NEW &#8220;AI AGENT&#8221; Will Change The WORLD! (Jim Fan)&#8221; by TheAIGRID<br \/>\n<iframe title=\"YouTube video player\" src=\"https:\/\/www.youtube.com\/embed\/K1i8Y55DRvQ?si=t4vKqwzgsy7yjkub\" width=\"560\" height=\"315\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\"><\/iframe><\/p>\n<h3>Key Takeaways:<\/h3>\n<ol>\n<li><strong>Foundation Agent Concept<\/strong>: Jim Fan introduced the concept of a &#8220;Foundation Agent,&#8221; capable of operating across both virtual and physical worlds, not to be confused with AGI (Artificial General Intelligence).<\/li>\n<li><strong>Versatile Applications<\/strong>: The potential applications of Foundation Agents span across various domains, including video games, the metaverse, drones, and humanoid robots, demonstrating the agent&#8217;s versatility.<\/li>\n<li><strong>Single Model, Multiple Realities<\/strong>: The Foundation Agent aims to master skills in different realities, using a single model to seamlessly transition between virtual and physical environments.<\/li>\n<li><strong>Voyager<\/strong>: An AI agent developed by Nvidia that can play Minecraft professionally, demonstrating the ability to perform tasks, explore, and learn in an open-ended game environment without human intervention.<\/li>\n<li><strong>Coding as Action<\/strong>: Voyager utilizes a novel approach where coding acts as the mechanism for action within the game, converting 3D world interactions into textual representations and executing tasks through generated JavaScript code.<\/li>\n<li><strong>Self-Improvement Mechanism<\/strong>: Voyager features self-reflection mechanisms allowing it to learn from errors, improve, and expand its skillset autonomously, showcasing an advanced level of AI learning and adaptability.<\/li>\n<li><strong>Unsupervised Learning Objectives<\/strong>: The AI&#8217;s goal to obtain as many unique items as possible in Minecraft exemplifies an unsupervised learning approach, driving exploration and skill development without explicit human directives.<\/li>\n<li><strong>Simulation for Training<\/strong>: The use of simulations, such as Nvidia&#8217;s Omniverse and YouTube videos for training AI, underscores the importance of synthetic and real-world data in developing and refining AI capabilities.<\/li>\n<li><strong>Eureqa &#8211; Advanced Robot Manipulation<\/strong>: Nvidia&#8217;s development of a robot hand capable of performing complex tasks like pen spinning in simulation illustrates progress in robotics and the potential for AI to automate and enhance robotic programming and operation.<\/li>\n<li><strong>Future Directions and Challenges<\/strong>: The discussion touches on the potential for multi-agent cooperation, the strategic value of diverse training data, and the ongoing efforts to bridge the gap between simulation-based learning and real-world application.<\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"<p>Nvidia&#8217;s Jim Fan unveils the Foundation Agent, bridging virtual and physical worlds for applications in gaming, metaverse, and robotics, demonstrating AI&#8217;s evolving versatility.<\/p>\n","protected":false},"author":3,"featured_media":5273,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[40],"tags":[],"class_list":["post-5272","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai","category-40","description-off"],"_links":{"self":[{"href":"https:\/\/wam.vn\/en\/wp-json\/wp\/v2\/posts\/5272","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/wam.vn\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/wam.vn\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/wam.vn\/en\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/wam.vn\/en\/wp-json\/wp\/v2\/comments?post=5272"}],"version-history":[{"count":1,"href":"https:\/\/wam.vn\/en\/wp-json\/wp\/v2\/posts\/5272\/revisions"}],"predecessor-version":[{"id":5274,"href":"https:\/\/wam.vn\/en\/wp-json\/wp\/v2\/posts\/5272\/revisions\/5274"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/wam.vn\/en\/wp-json\/wp\/v2\/media\/5273"}],"wp:attachment":[{"href":"https:\/\/wam.vn\/en\/wp-json\/wp\/v2\/media?parent=5272"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/wam.vn\/en\/wp-json\/wp\/v2\/categories?post=5272"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/wam.vn\/en\/wp-json\/wp\/v2\/tags?post=5272"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}