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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":4,"collection":249,"articles_id":250,"cover_url":251,"cover_url_1_1":252,"title":253,"summary":254,"author":28,"content":255,"popular":256,"list":319,"category":376,"tag":377},366,"2023-12-19","#ASI","Dea0LL8EGqaFmfo--JR10A","article_res/cover/99652fdedf6736263912ef5781e89f26.jpeg","article_res/cover/5fef09893d95127375cd40ad29687536.jpeg","ASI's Journey from GPT-2 to Artificial Superintelligence","It is feasible to make empirical progress today on a fundamental challenge of aligning superhuman models.","\u003Cdiv class=\"rich_media_content js_underline_content\n                       autoTypeSetting24psection\n            \" id=\"js_content\">\u003Cp data-tool=\"mdnice编辑器\" style='margin-bottom: 0px;padding-top: 8px;padding-bottom: 8px;color: black;font-family: Optima-Regular, Optima, PingFangSC-light, PingFangTC-light, \"PingFang SC\", Cambria, Cochin, Georgia, Times, \"Times New Roman\", serif;font-size: 16px;letter-spacing: normal;text-align: left;text-wrap: wrap;line-height: 26px;'>With the help of Claude2, I read a paper from OpenAI titled \"Weak-to-Strong Generalization: Eliciting Strong Capabilities with Weak Supervision.\" The link is https://cdn.openai.com/papers/weak-to-strong-generalization.pdf\u003C/p>\u003Cp data-tool=\"mdnice编辑器\" style='margin-bottom: 0px;padding-top: 8px;padding-bottom: 8px;color: black;font-family: Optima-Regular, Optima, PingFangSC-light, PingFangTC-light, \"PingFang SC\", Cambria, Cochin, Georgia, Times, \"Times New Roman\", serif;font-size: 16px;letter-spacing: normal;text-align: left;text-wrap: wrap;line-height: 26px;'>This paper provides an important perspective on the development of artificial superintelligence (ASI): the concept of \"weak-to-strong learning,\" where weaker AI models supervise more powerful ones. For example, whether humans can train smarter intelligences in the future.\u003C/p>\u003Cp style=\"text-align: center;\">\u003Cimg class=\"rich_pages wxw-img\" data-galleryid=\"\" data-imgfileid=\"100002856\" data-ratio=\"0.44814814814814813\" data-s=\"300,640\" data-type=\"png\" data-w=\"1080\" style=\"\" src=\"./assets/17423813995060.48840219335076474.png\">\u003C/p>\u003Cp data-tool=\"mdnice编辑器\" style='margin-bottom: 0px;padding-top: 8px;padding-bottom: 8px;color: black;font-family: Optima-Regular, Optima, PingFangSC-light, PingFangTC-light, \"PingFang SC\", Cambria, Cochin, Georgia, Times, \"Times New Roman\", serif;font-size: 16px;letter-spacing: normal;text-align: left;text-wrap: wrap;line-height: 26px;'>The paper uses simple methods such as fine-tuning GPT-4 with GPT-2 level supervisors, which significantly improves generalization capabilities and recovers performance close to GPT-3.5 levels in NLP tasks.\u003C/p>\u003Cp data-tool=\"mdnice编辑器\" style='margin-bottom: 0px;padding-top: 8px;padding-bottom: 8px;color: black;font-family: Optima-Regular, Optima, PingFangSC-light, PingFangTC-light, \"PingFang SC\", Cambria, Cochin, Georgia, Times, \"Times New Roman\", serif;font-size: 16px;letter-spacing: normal;text-align: left;text-wrap: wrap;line-height: 26px;'>The main findings include:\u003C/p>\u003Col data-tool=\"mdnice编辑器\" class=\"list-paddingleft-1\" style='margin-top: 8px;margin-bottom: 8px;padding-left: 25px;width: 557.438px;color: black;font-family: Optima-Regular, Optima, PingFangSC-light, PingFangTC-light, \"PingFang SC\", Cambria, Cochin, Georgia, Times, \"Times New Roman\", serif;font-size: 16px;letter-spacing: normal;text-align: left;text-wrap: wrap;'>\u003Cli>\u003Csection style=\"margin-top: 5px;margin-bottom: 5px;line-height: 26px;color: rgb(1, 1, 1);\">\u003Cp style=\"padding-top: 8px;padding-bottom: 8px;line-height: 26px;color: black;\">: Strong models trained using weak model labels outperform the weak models but still fall short compared to strong models trained directly with real labels.\u003C/p>\u003C/section>\u003C/li>\u003Cli>\u003Csection style=\"margin-top: 5px;margin-bottom: 5px;line-height: 26px;color: rgb(1, 1, 1);\">\u003Cp style=\"padding-top: 8px;padding-bottom: 8px;line-height: 26px;color: black;\">: Methods like auxiliary confidence loss and guided learning can significantly improve weak-to-strong generalization. For instance, these methods can recover about 80% of the performance gap between weak and strong models in NLP tasks.\u003C/p>\u003C/section>\u003C/li>\u003Cli>\u003Csection style=\"margin-top: 5px;margin-bottom: 5px;line-height: 26px;color: rgb(1, 1, 1);\">\u003Cp style=\"padding-top: 8px;padding-bottom: 8px;line-height: 26px;color: black;\">: Strong models may overfit the errors in weak supervision, and larger models find it harder to mimic the weaknesses of smaller models.\u003C/p>\u003C/section>\u003C/li>\u003C/ol>\u003Cp data-tool=\"mdnice编辑器\" style='margin-bottom: 0px;padding-top: 8px;padding-bottom: 8px;color: black;font-family: Optima-Regular, Optima, PingFangSC-light, PingFangTC-light, \"PingFang SC\", Cambria, Cochin, Georgia, Times, \"Times New Roman\", serif;font-size: 16px;letter-spacing: normal;text-align: left;text-wrap: wrap;line-height: 26px;'>Researchers conducted tests on NLP, chess, and reward modeling tasks using pre-trained language models from the GPT-4 series. The results show that when powerful pre-trained models are naively fine-tuned on labels generated by weak models, their performance consistently surpasses that of the weak supervisor. This phenomenon is referred to as \"weak-to-strong generalization.\"\u003C/p>\u003Cp style=\"text-align: center;\">\u003Cimg class=\"rich_pages wxw-img\" data-galleryid=\"\" data-imgfileid=\"100002857\" data-ratio=\"0.38333333333333336\" data-s=\"300,640\" data-type=\"png\" data-w=\"1080\" style=\"\" src=\"./assets/17423813995050.4409029948587595.png\">\u003C/p>\u003Cp data-tool=\"mdnice编辑器\" style='margin-bottom: 0px;padding-top: 8px;padding-bottom: 8px;color: black;font-family: Optima-Regular, Optima, PingFangSC-light, PingFangTC-light, \"PingFang SC\", Cambria, Cochin, Georgia, Times, \"Times New Roman\", serif;font-size: 16px;letter-spacing: normal;text-align: left;text-wrap: wrap;line-height: 26px;'>However, this method does not fully unleash the full potential of powerful models. This suggests that current alignment techniques, such as reinforcement learning with human feedback (RLHF), may be difficult to apply to superhuman-level models unless further improvements are made. Human supervision alone seems insufficient for training advanced ASI systems. Despite demonstrating weak-to-strong generalization capabilities, powerful AI models still have significant gaps compared to their full potential.\u003C/p>\u003Cp style=\"display: none;\">\u003Cmp-style-type data-value=\"3\">\u003C/mp-style-type>\u003C/p>\u003C/div>",[257,265,273,280,288,296,304,312],{"id":258,"title_md5":259,"publish_date":260,"author_md5":261,"is_original":23,"collection":65,"summary_md5":262,"cover_url":263,"cover_url_1_1":264},442,"a0583e1b06f6df1c9341bc5d689426c4","2023-06-22","bc27fa490c4d0d525bac812fc0793534","7fe2002395be0cb91f1f3a4017cbea4c","article_res/cover/2dd0c4e3d8f15e1059b9d9d6db552590.jpeg","article_res/cover/495132f609e2d722e8fb11ba67b7d945.jpeg",{"id":266,"title_md5":267,"publish_date":268,"author_md5":261,"is_original":4,"collection":269,"summary_md5":270,"cover_url":271,"cover_url_1_1":272},371,"222a6c9a9fe91b865cc08f8fa3d6e985","2023-12-14","#AI Avatar #Lip Sync #Microsoft","38950e49ef14f35015ef1cda0a478ca6","article_res/cover/5290cb4a28967eb073a9f721afe69641.jpeg","article_res/cover/8ea9fffb45ec2664c26209136f961b6a.jpeg",{"id":274,"title_md5":275,"publish_date":276,"author_md5":261,"is_original":23,"collection":5,"summary_md5":277,"cover_url":278,"cover_url_1_1":279},412,"cbf43706fafce6a21828b18631d306ce","2023-09-09","1ea71279dd86997b72b064fd0085a0af","article_res/cover/2a7cc4836df072904a7c6587411fd1c8.jpeg","article_res/cover/6e99c94d589e69cff3566829cc098202.jpeg",{"id":281,"title_md5":282,"publish_date":283,"author_md5":284,"is_original":4,"collection":5,"summary_md5":285,"cover_url":286,"cover_url_1_1":287},570,"c77be8405365c16a76df817a892efc0b","2022-04-16","8b3607d0f4181a3cb6ffdccf7185f09b","eb49311cd53f5240e10189fa27392840","article_res/cover/0e3abd5e8012a43838e36dd9c78671f7.jpeg","article_res/cover/a3c3af255bb3c07f731de4de77669fcd.jpeg",{"id":289,"title_md5":290,"publish_date":291,"author_md5":261,"is_original":23,"collection":292,"summary_md5":293,"cover_url":294,"cover_url_1_1":295},186,"4921e6b76d4a6be510b83e11d133cf1f","2024-08-27","#AI Agent #Devin","c3a06be338a881002beacf5f36577314","article_res/cover/a2f87c54ec773eba5f90e7a037330b59.jpeg","article_res/cover/60b5058c74f6698907829caf180ad142.jpeg",{"id":297,"title_md5":298,"publish_date":299,"author_md5":261,"is_original":4,"collection":300,"summary_md5":301,"cover_url":302,"cover_url_1_1":303},129,"218c87c80e3686e8bea47e280a11855c","2024-11-12","#OpenAI #AI Code Generator #LLM #o1","fcd1469c61cdf5c7c75be25e02cdb16f","article_res/cover/6654db2a98aadeea109fc3364da389c7.jpeg","article_res/cover/b1e5e6d6a62fb9b205ac09b16c6a25da.jpeg",{"id":305,"title_md5":306,"publish_date":307,"author_md5":308,"is_original":4,"collection":5,"summary_md5":309,"cover_url":310,"cover_url_1_1":311},495,"9b15ccc6efafbde434556a70af4440d0","2023-03-30","9f3428c4d2d88afac6a16510d115e41c","8c3a91662be7ddf873148e266df78686","article_res/cover/9cc1e0b26ea462d3277b0d98b9a74620.jpeg","article_res/cover/8ecf7bdc377a506ebe83640c883cdff7.jpeg",{"id":313,"title_md5":314,"publish_date":315,"author_md5":261,"is_original":23,"collection":5,"summary_md5":316,"cover_url":317,"cover_url_1_1":318},258,"e57e836374325bce061232f12fc59efb","2024-06-01","cb7475904556d84c4ae3dcce1c09604b","article_res/cover/1bfdd9f722ca4937424193644925fa60.jpeg","article_res/cover/fee4db41bf846593e4b8ffea8d53a8be.jpeg",{"related":320,"small":361},[321,329,337,345,353],{"id":322,"publish_date":323,"is_original":23,"collection":324,"cover_url":325,"cover_url_1_1":326,"title":327,"summary":328,"author":28},28,"2025-02-24","#Andrej Karpathy #Deep Dive into LLMs #LLM #RL #DeepSeek","article_res/cover/db62a8c43fa565112c0aefa8776e1de2.jpeg","article_res/cover/cbb4289951cb6a0cda64f8fd913a2e23.jpeg","DeepSeek-R1 - 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