[{"data":1,"prerenderedAt":382},["ShallowReactive",2],{"$fgukOamtKU1RtUiMFsqdObttmqPPQz0uc7bl_gj_LyX0":3,"$fzk0TlLWdKAHWk3fWF457QNVKWEHx2QggA6XjO0qERAQ":245,"article-444":381},{"code":4,"msg":5,"data":6},0,"",{"category":7,"tag":11,"popular":19,"latest":86,"banner":126,"list":151,"cache":244},[8,9,10],"Agent","OpenAI","LLM",[8,12,13,14,9,10,15,16,17,18],"Google","Nvidia","Claude","DeepSeek","OCR","Chat","Generator",[20,29,37,45,54,62,70,79],{"id":21,"publish_date":22,"is_original":23,"collection":5,"cover_url":24,"cover_url_1_1":25,"title":26,"summary":27,"author":28},411,"2023-09-10",1,"article_res/cover/451ef50c225a8dc61c4336506794d13b.jpeg","article_res/cover/3ba9dc7a72f87d40b20fc2d225289ee3.jpeg","Idealism","Reality is created by the mind, we can change our reality by changing our mind. - Plato","Renee's Entrepreneurial Journey",{"id":30,"publish_date":31,"is_original":23,"collection":32,"cover_url":33,"cover_url_1_1":34,"title":35,"summary":36,"author":28},108,"2024-12-07","#LLM #AGI #AI Agent","article_res/cover/0039044422e4ec9f61c18e8ee1693bb0.jpeg","article_res/cover/4220971b108a91d21407d87bb02fbaa6.jpeg","Freysa.ai: The World's First Adversarial AI Agent Game","说服 Freysa 把钱包里的钱都拿出来",{"id":38,"publish_date":39,"is_original":23,"collection":40,"cover_url":41,"cover_url_1_1":42,"title":43,"summary":44,"author":28},12,"2025-03-09","#Oxford #Reasoning #LLM #Tool Use","article_res/cover/d448e9b3617a0b5302e1bd10c438bca9.jpeg","article_res/cover/864a468f9cc4c9317efadb3811909888.jpeg","Agentic Reasoning Framework - Significantly enhance the reasoning ability of LLMs through the integration of external tools using agents","Agentic Reasoning: Reasoning LLMs with Tools for Deep Research",{"id":46,"publish_date":47,"is_original":4,"collection":48,"cover_url":49,"cover_url_1_1":50,"title":51,"summary":52,"author":53},480,"2023-04-14","#Stable Diffusion","article_res/cover/0bdbe7cb1de4a78e54536e5d9afa7ec9.jpeg","article_res/cover/b3d6ffec0608dcfaf18c5a69906d1490.jpeg","【AIGC Learning】Generate Prompts Using Word Graphs - Stable Diffusion Web UI Series 13","AI will become a powerful tool in education, transforming the way we learn and deliver instruction.  \n- Reid Hoffman","--",{"id":55,"publish_date":56,"is_original":4,"collection":57,"cover_url":58,"cover_url_1_1":59,"title":60,"summary":61,"author":28},413,"2023-09-08","#Neuroscience","article_res/cover/74f8302d78a23d9430f22171eae136b6.jpeg","article_res/cover/87ca08af81bb304746be5261160964c0.jpeg","Can machines be conscious?","Do we have an ethical obligation to not turn off conscious machines? 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No. I don't lose any sleep over unplugging a conscious machine.\n- Jeff Hawkins, \"A Thousand Brains\"",{"id":63,"publish_date":64,"is_original":23,"collection":65,"cover_url":66,"cover_url_1_1":67,"title":68,"summary":69,"author":28},178,"2024-09-09","#Entrepreneurship","article_res/cover/a7224f025b55d1820408085faef63079.jpeg","article_res/cover/11a9995b096cbf64465ef01b8673b154.jpeg","37signals company","This damn sense of relaxation",{"id":71,"publish_date":72,"is_original":4,"collection":73,"cover_url":74,"cover_url_1_1":75,"title":76,"summary":77,"author":78},460,"2023-05-12","#Google","article_res/cover/b970687b12faa52da976f91248c2aa7b.jpeg","article_res/cover/d1e71b52cfd2c63bc6e71f3e85ff135c.jpeg","Learn what BRC-20 and Ordinals are using Google Bard","Ordinals - a new protocol that allows users to store arbitrary data on the Bitcoin blockchain","Google Bard mainly writes",{"id":80,"publish_date":81,"is_original":23,"collection":5,"cover_url":82,"cover_url_1_1":83,"title":84,"summary":85,"author":28},309,"2024-03-26","article_res/cover/9877f95894ee88532d0e6012c23a2df3.jpeg","article_res/cover/20092164ddc109ce6ae56b1984246751.jpeg","Learning the Cancun Upgrade with lepton and perplexity","Building a quick conversation-based search demo with Lepton AI.",[87,95,103,111,119],{"id":88,"publish_date":89,"is_original":23,"collection":90,"cover_url":91,"cover_url_1_1":92,"title":93,"summary":94,"author":28},627,"2025-03-20","#AI Avatar #AI Video Generation","article_res/cover/d95481358f73924989f8c4ee9c75d1c8.jpeg","article_res/cover/b74bc0fab01f8b6a6aa87696c0c3ed8b.jpeg","DisPose: Generating Animated Videos by Driving Video with Reference Images","DisPose is a controllable human image animation method that enhances video generation.",{"id":96,"publish_date":97,"is_original":23,"collection":98,"cover_url":99,"cover_url_1_1":100,"title":101,"summary":102,"author":28},626,"2025-03-21","#Deep Dive into LLMs #LLM #RL #Andrej Karpathy #AlphaGo","article_res/cover/446553a5c8f8f2f07d97b20eaee84e56.jpeg","article_res/cover/e6c2823409c9b34624064b9acbaca6f1.jpeg","AlphaGo and the Power of Reinforcement Learning - 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An automated website-building platform composed of multiple AI Agents","Your 24/7 AI Team | Dream, Chat, Create.",{"title":182,"list":183},"OPENAI",[184,191,199,206],{"id":185,"publish_date":167,"is_original":23,"collection":186,"cover_url":187,"cover_url_1_1":188,"title":189,"summary":190,"author":28},619,"#OpenAI #AI Image Generator #4o #MMM #AR Transformer","article_res/cover/2faffc97fcecf3151552cb0fd3206d89.jpeg","article_res/cover/1133cb4948af44cee2e7fbe79efb69e5.jpeg","The native image function of GPT-4o is officially launched","Introducing 4o Image Generation",{"id":192,"publish_date":193,"is_original":4,"collection":194,"cover_url":195,"cover_url_1_1":196,"title":197,"summary":198,"author":28},434,"2023-07-15","#Anthropic #OpenAI #Google #AI Code Generator #Claude","article_res/cover/e1b6f600a2b9f262a4392684e5f2ce25.jpeg","article_res/cover/6e1772e83f78f9a351ab23d3e414adee.jpeg","Latest Updates on Google Bard /Anthropic Claude2 / ChatGPT Code Interpreter","We want our models to use their programming skills to provide more natural interfaces to the basic functions of our computers.  \n - OpenAI",{"id":200,"publish_date":201,"is_original":4,"collection":146,"cover_url":202,"cover_url_1_1":203,"title":204,"summary":205,"author":28},417,"2023-08-24","article_res/cover/bccf897d50a88b18364e35f7466387e0.jpeg","article_res/cover/2f871085c1073717c1703ae86e18056f.jpeg","The GPT-3.5 Turbo fine-tuning (fine-tuning function) has been released～","Developers can now bring their own data to customize GPT-3.5 Turbo for their use cases.",{"id":207,"publish_date":208,"is_original":4,"collection":209,"cover_url":210,"cover_url_1_1":211,"title":212,"summary":213,"author":28},407,"2023-09-22","#OpenAI #AI Image Generator","article_res/cover/c59005e903d35cfc32346e2756e2728a.jpeg","article_res/cover/ba011d265e6d84b5c8cb6fd6b757b6cc.jpeg","Dall-E 3","DALL·E 3 understands significantly more nuance and detail, allowing you to easily translate your ideas into images.",[215,221,241],{"title":10,"list":216},[217,218,219,220],{"id":96,"publish_date":97,"is_original":23,"collection":98,"cover_url":99,"cover_url_1_1":100,"title":101,"summary":102,"author":28},{"id":104,"publish_date":105,"is_original":23,"collection":106,"cover_url":107,"cover_url_1_1":108,"title":109,"summary":110,"author":28},{"id":112,"publish_date":113,"is_original":23,"collection":114,"cover_url":115,"cover_url_1_1":116,"title":117,"summary":118,"author":28},{"id":166,"publish_date":167,"is_original":23,"collection":168,"cover_url":169,"cover_url_1_1":170,"title":171,"summary":172,"author":28},{"title":222,"list":223},"GOOGLE",[224,225,226,234],{"id":120,"publish_date":113,"is_original":23,"collection":121,"cover_url":122,"cover_url_1_1":123,"title":124,"summary":125,"author":28},{"id":166,"publish_date":167,"is_original":23,"collection":168,"cover_url":169,"cover_url_1_1":170,"title":171,"summary":172,"author":28},{"id":227,"publish_date":228,"is_original":23,"collection":229,"cover_url":230,"cover_url_1_1":231,"title":232,"summary":233,"author":28},615,"2025-03-30","#AI Researcher #AI Science #HKU #Google #AI Agent","article_res/cover/21fadf906067714bb0db31ae13a77c15.jpeg","article_res/cover/2697999a72bd26b22e85f0e92936d3ed.jpeg","AI-Researcher: LLM-driven全自动 scientific research assistant","AI-Researcher: Fully-Automated Scientific Discovery with LLM Agents  \nOpen-Sourced Alternative to Google AI Co-Scientist",{"id":235,"publish_date":236,"is_original":23,"collection":73,"cover_url":237,"cover_url_1_1":238,"title":239,"summary":240,"author":28},463,"2023-05-09","article_res/cover/89800f207723acdb55fc53bf999ebdc9.jpeg","article_res/cover/5764f369b4accd8f83e94aa4c077a175.jpeg","The Smallville sandbox world - A town with 25 virtual residents","Believable proxies of human behavior can empower interactive apps: Immersive environment, Rehearsal space, Prototyping tool",{"title":242,"list":243},"NVIDIA",[],true,{"code":4,"msg":5,"data":246},{"id":247,"publish_date":248,"is_original":23,"collection":249,"articles_id":250,"cover_url":251,"cover_url_1_1":252,"title":253,"summary":254,"author":28,"content":255,"popular":256,"list":322,"category":379,"tag":380},444,"2023-06-12","#Microsoft","JWZ_8pqARMrvaHpoXdqSGA","article_res/cover/690a412030488f27ac4d4b845d99c36c.jpeg","article_res/cover/a6e3eaaef47d440b4c605f90875ae9a8.jpeg","HuggingGPT","HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face","\u003Cdiv class=\"rich_media_content js_underline_content\n                       autoTypeSetting24psection\n            \" id=\"js_content\">\u003Csection data-tool=\"mdnice编辑器\" data-website=\"https://www.mdnice.com\" style='font-size: 16px;color: black;padding: 0px 10px;line-height: 1.6;word-spacing: 0px;letter-spacing: 0px;word-break: break-word;overflow-wrap: break-word;text-align: left;font-family: Optima-Regular, Optima, PingFangSC-light, PingFangTC-light, \"PingFang SC\", Cambria, Cochin, Georgia, Times, \"Times New Roman\", serif;'>\u003Cp data-tool=\"mdnice编辑器\" style=\"font-size: 16px;padding-top: 8px;padding-bottom: 8px;margin: 0;line-height: 26px;color: black;\">Today, experience a tool that can handle multi-modal tasks: HuggingGPT. You can find more details at this link: https://huggingface.co/spaces/microsoft/HuggingGPT.\u003C/p>\u003Cp data-tool=\"mdnice编辑器\" style=\"font-size: 16px;padding-top: 8px;padding-bottom: 8px;margin: 0;line-height: 26px;color: black;\">\u003Cbr>\u003C/p>\u003Cp style=\"text-align: center;\">\u003Cimg class=\"rich_pages wxw-img\" data-galleryid=\"\" data-ratio=\"0.8205128205128205\" data-s=\"300,640\" data-type=\"png\" data-w=\"312\" style=\"\" src=\"./assets/17434960007550.9319043090172392.png\">\u003C/p>\u003Cbr>\u003Cp>\u003Cbr>\u003C/p>\u003Cp data-tool=\"mdnice编辑器\" style=\"font-size: 16px;padding-top: 8px;padding-bottom: 8px;margin: 0;line-height: 26px;color: black;\">The corresponding paper link is as follows: https://arxiv.org/abs/2303.17580.\u003C/p>\u003Cp data-tool=\"mdnice编辑器\" style=\"font-size: 16px;padding-top: 8px;padding-bottom: 8px;margin: 0;line-height: 26px;color: black;\">The main goal of the HuggingGPT system is to assist large language models (LLMs) in handling complex AI tasks.\u003C/p>\u003Cp data-tool=\"mdnice编辑器\" style=\"font-size: 16px;padding-top: 8px;padding-bottom: 8px;margin: 0;line-height: 26px;color: black;\">A brief introduction to how HuggingGPT works is as follows: First, it uses ChatGPT to perform task planning based on user needs; then, it selects appropriate models according to the functional descriptions provided by the Hugging Face platform; next, it executes each subtask using the selected AI model; finally, it generates a summary of the response based on the execution results. This method enables HuggingGPT to effectively handle complex AI tasks across various modalities and domains, including challenging tasks in language, vision, speech, and more, achieving remarkable results.\u003C/p>\u003Cp style=\"text-align: center;\">\u003Cimg class=\"rich_pages wxw-img\" data-galleryid=\"\" data-ratio=\"0.45\" data-s=\"300,640\" data-type=\"png\" data-w=\"1080\" style=\"\" src=\"./assets/17434960007480.26392456867948666.png\">\u003C/p>\u003Cp>\u003Cbr>\u003C/p>\u003Cp data-tool=\"mdnice编辑器\" style=\"font-size: 16px;padding-top: 8px;padding-bottom: 8px;margin: 0;line-height: 26px;color: black;\">HuggingGPT, as a promising new approach, can assist LLMs in achieving general artificial intelligence. By combining LLMs with expert models, it helps LLMs learn new skills and knowledge, thereby enabling them to better understand the world around them.\u003C/p>\u003Cp data-tool=\"mdnice编辑器\" style=\"font-size: 16px;padding-top: 8px;padding-bottom: 8px;margin: 0;line-height: 26px;color: black;\">This paper also summarizes some of the key features of HuggingGPT:\u003C/p>\u003Cul data-tool=\"mdnice编辑器\" style=\"margin-top: 8px;margin-bottom: 8px;padding-left: 25px;color: black;list-style-type: disc;\" class=\"list-paddingleft-1\">\u003Cli>\u003Csection style=\"margin-top: 5px;margin-bottom: 5px;line-height: 26px;text-align: left;color: rgb(1,1,1);font-weight: 500;\">HuggingGPT is a collaborative system composed of an LLM as the controller and numerous expert models as collaborative executors.\u003C/section>\u003C/li>\u003Cli>\u003Csection style=\"margin-top: 5px;margin-bottom: 5px;line-height: 26px;text-align: left;color: rgb(1,1,1);font-weight: 500;\">The paper proposes an inter-model collaboration protocol that fully leverages the strengths of large language models and expert models. In this protocol, the large language model serves as the central hub for planning and decision-making, while smaller models act as executors for specific tasks, providing a new path for designing general AI models.\u003C/section>\u003C/li>\u003Cli>\u003Csection style=\"margin-top: 5px;margin-bottom: 5px;line-height: 26px;text-align: left;color: rgb(1,1,1);font-weight: 500;\">The workflow of HuggingGPT includes four stages: task planning, task execution, task evaluation, and task learning.\u003C/section>\u003C/li>\u003Cli>\u003Csection style=\"margin-top: 5px;margin-bottom: 5px;line-height: 26px;text-align: left;color: rgb(1,1,1);font-weight: 500;\">By integrating the Hugging Face Hub with over 400 task-specific models centered around ChatGPT, HuggingGPT can handle generalized AI tasks, offering users multimodal and reliable conversational services through open model collaboration.\u003C/section>\u003C/li>\u003C/ul>\u003Cp data-tool=\"mdnice编辑器\" style=\"font-size: 16px;padding-top: 8px;padding-bottom: 8px;margin: 0;line-height: 26px;color: black;\">HuggingGPT uses a demonstration-based parsing method to better understand the intent and standards of task planning. It has been proven effective in solving various complex AI tasks, including question answering, summarization, and translation. Through extensive experiments on multiple challenging AI tasks in language, vision, speech, and cross-modality, HuggingGPT's capabilities are demonstrated. The results show that HuggingGPT can understand and solve complex tasks from multiple modalities and domains.\u003C/p>\u003Cp data-tool=\"mdnice编辑器\" style=\"font-size: 16px;padding-top: 8px;padding-bottom: 8px;margin: 0;line-height: 26px;color: black;\">Let me give an example. For instance, if there are two pictures and I want to recreate the action in picture 1 in picture 2, we can manually operate using Controlnet in the stable diffusion webui, but we can also provide natural language instructions to HuggingGPT, allowing it to call upon drawing AI to execute the task.\u003C/p>\u003Cp style=\"text-align: center;\">\u003Cimg class=\"rich_pages wxw-img\" data-galleryid=\"\" data-ratio=\"1.1055555555555556\" data-s=\"300,640\" data-type=\"png\" data-w=\"1080\" style=\"\" src=\"./assets/17434960005940.625119784400552.png\">\u003C/p>\u003Cp>\u003Cbr>\u003C/p>\u003Cp data-tool=\"mdnice编辑器\" style=\"font-size: 16px;padding-top: 8px;padding-bottom: 8px;margin: 0;line-height: 26px;color: black;\">and there is still a significant gap. We look forward to more exciting products coming out in the future.\u003C/p>\u003Cp style=\"text-align: center;\">\u003Cimg class=\"rich_pages wxw-img\" data-galleryid=\"\" data-ratio=\"0.992\" data-s=\"300,640\" data-type=\"jpeg\" data-w=\"1000\" style=\"\" src=\"./assets/17434959999280.9502425577524576.jpeg\">\u003C/p>\u003Cp>\u003Cbr>\u003C/p>\u003C/section>\u003Cp style=\"display: none;\">\u003Cmp-style-type data-value=\"3\">\u003C/mp-style-type>\u003C/p>\u003C/div>",[257,265,273,282,289,298,306,314],{"id":258,"title_md5":259,"publish_date":260,"author_md5":261,"is_original":4,"collection":5,"summary_md5":262,"cover_url":263,"cover_url_1_1":264},550,"03b358c1488c7320c06d08daf8c4d560","2022-05-06","8b3607d0f4181a3cb6ffdccf7185f09b","66c6b81db6f58399865e0c7b3ae697c2","article_res/cover/d8cfe9c39f6f1cc3e26ff8354e6fa4c6.jpeg","article_res/cover/8a684519f2ba3c7f4dd37f79ffe15e24.jpeg",{"id":266,"title_md5":267,"publish_date":268,"author_md5":269,"is_original":4,"collection":5,"summary_md5":270,"cover_url":271,"cover_url_1_1":272},433,"b7284af2c7824b9e0866ceed0094b187","2023-07-16","aaa8ca17ae2e86c309dea80b10f6e68d","a3d7417bb9de0ade6b15493600246eb1","article_res/cover/ca3482d96ae636eab811ceea3f688b18.jpeg","article_res/cover/5cc44dd7e13714ecd8e3df63140ee6b9.jpeg",{"id":274,"title_md5":275,"publish_date":276,"author_md5":277,"is_original":23,"collection":278,"summary_md5":279,"cover_url":280,"cover_url_1_1":281},185,"4e8d473a54375e054d00d774d4cc7a06","2024-08-29","bc27fa490c4d0d525bac812fc0793534","#AI in Science #Nature","32f357d59e16288d0ec249b280abb8f2","article_res/cover/194ffd725876fa7c3a8d36841d737fb2.jpeg","article_res/cover/59418e8cd0d64a3009356da529632030.jpeg",{"id":283,"title_md5":284,"publish_date":285,"author_md5":261,"is_original":4,"collection":5,"summary_md5":286,"cover_url":287,"cover_url_1_1":288},521,"c5ca5171c8fe41a9a1171e5021ffb886","2022-06-05","282918086e35b32061070ef44a369438","article_res/cover/4da966ae1a3ad1ec2e9fce1682dfab6d.jpeg","article_res/cover/50550d3da0daf64756855d267ea657f2.jpeg",{"id":290,"title_md5":291,"publish_date":292,"author_md5":293,"is_original":4,"collection":294,"summary_md5":295,"cover_url":296,"cover_url_1_1":297},588,"cddaa25577d3401f776d1bc2feff1bde","2022-03-29","70997a2931a60561e615f5171df5f9a5","#Philosophy","b13f09ac7bf387db64e2afa0882c7f63","article_res/cover/8a394d06bcf425e5d8f0c2b445313047.jpeg","article_res/cover/edded5e9be422882ea540097024968ff.jpeg",{"id":299,"title_md5":300,"publish_date":301,"author_md5":277,"is_original":23,"collection":302,"summary_md5":303,"cover_url":304,"cover_url_1_1":305},36,"7ac2688dcb2412a28f10570ab0fc0ca2","2025-02-17","#Baidu #AI Avatar","bfc395b4cdb996740934b1e9328f30c9","article_res/cover/2f0a10000836de9daa5902903690697c.jpeg","article_res/cover/19bcf2b18f917a8e7e375cb69a00687d.jpeg",{"id":307,"title_md5":308,"publish_date":309,"author_md5":277,"is_original":23,"collection":310,"summary_md5":311,"cover_url":312,"cover_url_1_1":313},250,"4b1f4ea79f9a2fc09e0215ab9f734403","2024-06-10","#Buffett","5d486a61280ae734954903488db589a5","article_res/cover/c9692e2e7fee823ceb6e502de7b605a3.jpeg","article_res/cover/1e32882d197c58725ec0d28c0bf797ad.jpeg",{"id":315,"title_md5":316,"publish_date":317,"author_md5":318,"is_original":4,"collection":65,"summary_md5":319,"cover_url":320,"cover_url_1_1":321},357,"5987d93d380ce74d55aff7b407182ae7","2023-12-28","a10a5817af75d574042096c47b0c0090","4ac9612f97613ea2a1724acf8a9281b8","article_res/cover/d8cf9384170d6b77fff804e1482ceee6.jpeg","article_res/cover/e242f7853a2f846e17e12efcf4f9fa61.jpeg",{"related":323,"small":364},[324,333,341,348,356],{"id":325,"publish_date":326,"is_original":4,"collection":327,"cover_url":328,"cover_url_1_1":329,"title":330,"summary":331,"author":332},601,"2022-03-16","#Psychology","article_res/cover/ea61315067c2244c9727afe4bdf9a11b.jpeg","article_res/cover/17c29fb73778c34dabe6b5148aa4321d.jpeg","Raw egg and boiled egg","When we feel fragile, we’re like raw eggs; 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