[{"data":1,"prerenderedAt":373},["ShallowReactive",2],{"$fgukOamtKU1RtUiMFsqdObttmqPPQz0uc7bl_gj_LyX0":3,"$fKJCIl53P-985ZO0PT7xCYDvsjGQqe1erlxllJiD7WZQ":245,"article-28":372},{"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 - Andrej Karpathy's Deep Dive on LLMs (Part 9)","Simply learning from humans will never surpass human capabilities.",{"id":104,"publish_date":105,"is_original":23,"collection":106,"cover_url":107,"cover_url_1_1":108,"title":109,"summary":110,"author":28},625,"2025-03-22","#Deep Dive into LLMs #LLM #RL #RLHF #Andrej Karpathy","article_res/cover/8da81d38b1e5cf558a164710fd8a5389.jpeg","article_res/cover/96f028d76c362a99a0dd56389e8f7a9b.jpeg","Reinforcement Learning from Human Feedback (RLHF) - Andrej Karpathy's Deep Dive on LLMs (Part 10)","Fine-Tuning Language Models from Human Preferences",{"id":112,"publish_date":113,"is_original":23,"collection":114,"cover_url":115,"cover_url_1_1":116,"title":117,"summary":118,"author":28},624,"2025-03-23","#Deep Dive into LLMs #LLM #Andrej Karpathy #AI Agent #MMM","article_res/cover/a5e7c3d48bb09109684d6513287c661d.jpeg","article_res/cover/d3f22b7c0ab8d82fd2da457a299e0773.jpeg","The Future of Large Language Models - Andrej Karpathy's In-Depth Explanation of LLM (Part 11)","preview of things to come",{"id":120,"publish_date":113,"is_original":23,"collection":121,"cover_url":122,"cover_url_1_1":123,"title":124,"summary":125,"author":28},623,"#Google #Voe #AI Video Generation","article_res/cover/c44062fea0f336c2b96b3928292392c2.jpeg","article_res/cover/a041041c69092ad3db191c5bf3ff981b.jpeg","Trial of Google's video generation model VOE2","Our state-of-the-art video generation model",[127,135,143],{"id":128,"publish_date":129,"is_original":23,"collection":130,"cover_url":131,"cover_url_1_1":132,"title":133,"summary":134,"author":28},300,"2024-04-16","#AI in Science #AGI","article_res/cover/6bf01e793e0f33e848572412eebdf9b0.jpeg","article_res/cover/91a5ee21dafecb914fabeb9430d46ec1.jpeg","Would Einstein lose his job - AI and Quantum Computing: A Glimpse into the Near Future","So Einstein's job is still safe.",{"id":136,"publish_date":137,"is_original":23,"collection":138,"cover_url":139,"cover_url_1_1":140,"title":141,"summary":142,"author":28},101,"2024-12-14","#Nvidia #AI 3D Generator","article_res/cover/693e07c85980c5c0c8fde3f037733f23.jpeg","article_res/cover/9ea8edff2d5d303ff3fffff3f6f9c3d9.jpeg","NVIDIA's open-source 3D project LLaMA-Mesh","LLaMA-Mesh: Unifying 3D Mesh Generation with Language Models",{"id":144,"publish_date":145,"is_original":23,"collection":146,"cover_url":147,"cover_url_1_1":148,"title":149,"summary":150,"author":28},131,"2024-11-10","#OpenAI","article_res/cover/87f8ed353ce39f31960e7cdfaf075a35.jpeg","article_res/cover/f597a63935f5cd32e484b4aadd6019e8.jpeg","ChatGPT has launched the Search function","Get fast, timely answers with links to relevant web sources.",{"big":152,"small":214},[153,181],{"title":154,"list":155},"AGENT",[156,157,165,173],{"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":158,"publish_date":159,"is_original":23,"collection":160,"cover_url":161,"cover_url_1_1":162,"title":163,"summary":164,"author":28},622,"2025-03-24","#OWL #AI Agent #MAS #MCP #CUA","article_res/cover/cb50ca7f2bf4d1ed50202d7406e1c19a.jpeg","article_res/cover/4aa7aa3badfacf3cc84121334f1050dd.jpeg","OWL: Multi-agent collaboration","OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation",{"id":166,"publish_date":167,"is_original":23,"collection":168,"cover_url":169,"cover_url_1_1":170,"title":171,"summary":172,"author":28},620,"2025-03-26","#LLM #Google #Gemini #AI Agent","article_res/cover/53751a6dbbe990b1eb0b63f3b062aed4.jpeg","article_res/cover/031344981f0a212ff82d1f3a64aa5756.jpeg","Gemini 2.5 Pro, claimed to be far ahead of the competition, has been released with great fanfare: comprehensively surpassing other LLMs and topping the global rankings","Gemini 2.5: Our most intelligent AI model",{"id":174,"publish_date":175,"is_original":23,"collection":176,"cover_url":177,"cover_url_1_1":178,"title":179,"summary":180,"author":28},616,"2025-03-29","#MAS #AI Agent #AI Coder #MetaGPT #MGX","article_res/cover/9dcd702ad2035902e5e77967c34a1f1e.jpeg","article_res/cover/0a97fc4a922753c8f46ff38792020df8.jpeg","MGX - 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":320,"category":370,"tag":371},28,"2025-02-24","#Andrej Karpathy #Deep Dive into LLMs #LLM #RL #DeepSeek","q6i_ZH8kziq4Jvem-6WYnw","article_res/cover/db62a8c43fa565112c0aefa8776e1de2.jpeg","article_res/cover/cbb4289951cb6a0cda64f8fd913a2e23.jpeg","DeepSeek-R1 - Andrej Karpathy in-depth explanation of LLM (Part 9)","DeepSeek R1","\u003Cdiv class=\"rich_media_content js_underline_content\n                       autoTypeSetting24psection\n            \" id=\"js_content\">\u003Cp style='box-sizing: border-box;margin: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;text-indent: 0px;padding: 8px 0px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-style: normal;font-variant-ligatures: normal;font-variant-caps: normal;font-weight: 400;orphans: 2;text-transform: none;widows: 2;word-spacing: 0px;-webkit-text-stroke-width: 0px;white-space: normal;background-color: rgb(255, 255, 255);text-decoration-thickness: initial;text-decoration-style: initial;text-decoration-color: initial;'>\u003Cspan leaf=\"\">Pre-training and SFT (Supervised Fine-Tuning) have existed for many years in the LLM training domain and are widely applied. However, the introduction of RL (Reinforcement Learning) training is a relatively newer attempt in this field currently, and it has not been fully standardized yet.\u003C/span>\u003C/p>\u003Cp style='box-sizing: border-box;margin: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;text-indent: 0px;padding: 8px 0px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-style: normal;font-variant-ligatures: normal;font-variant-caps: normal;font-weight: 400;orphans: 2;text-transform: none;widows: 2;word-spacing: 0px;-webkit-text-stroke-width: 0px;white-space: normal;background-color: rgb(255, 255, 255);text-decoration-thickness: initial;text-decoration-style: initial;text-decoration-color: initial;'>\u003Cspan leaf=\"\">Although the high-level concept of this stage is very simple—optimizing the model through trial and error learning—the details and mathematical principles are quite complex, including aspects such as how to select the optimal solution, controlling the amount of training, designing the distribution of prompts, and configuring the training runs.\u003C/span>\u003C/p>\u003Cp style='box-sizing: border-box;margin: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;text-indent: 0px;padding: 8px 0px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-style: normal;font-variant-ligatures: normal;font-variant-caps: normal;font-weight: 400;orphans: 2;text-transform: none;widows: 2;word-spacing: 0px;-webkit-text-stroke-width: 0px;white-space: normal;background-color: rgb(255, 255, 255);text-decoration-thickness: initial;text-decoration-style: initial;text-decoration-color: initial;'>\u003Cspan leaf=\"\">Recently, DeepSeek released a paper of significant importance, revealing its work on reinforcement learning fine-tuning for the first time, which provides stronger reasoning capabilities for LLMs. Through this disclosure, DeepSeek not only sparked industry interest in the application of reinforcement learning in LLMs but also provided necessary details to help other researchers reproduce the method and further explore based on it.\u003C/span>\u003C/p>\u003Csection style=\"text-align: center;\" nodeleaf=\"\">\u003Cimg class=\"rich_pages wxw-img js_insertlocalimg\" data-ratio=\"0.9398148148148148\" data-s=\"300,640\" data-type=\"png\" data-w=\"1080\" type=\"block\" data-imgfileid=\"100010241\" src=\"./assets/17423769993420.8105348761023026.png\">\u003C/section>\u003Ch3 style='box-sizing: border-box;margin: 30px 0px 15px;color: rgba(0, 0, 0, 0.85);font-weight: 500;cursor: pointer;padding: 0px;display: block;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-style: normal;font-variant-ligatures: normal;font-variant-caps: normal;letter-spacing: normal;orphans: 2;text-align: left;text-indent: 0px;text-transform: none;widows: 2;word-spacing: 0px;-webkit-text-stroke-width: 0px;white-space: normal;background-color: rgb(255, 255, 255);text-decoration-thickness: initial;text-decoration-style: initial;text-decoration-color: initial;'>\u003Cspan style=\"box-sizing: border-box;cursor: pointer;font-size: 20px;color: rgb(0, 0, 0);line-height: 1.5em;letter-spacing: 0em;text-align: left;font-weight: bold;display: block;\">\u003Cspan leaf=\"\">The Application of Reinforcement Learning in Language Models: A Breakthrough in Cognitive Strategies\u003C/span>\u003C/span>\u003C/h3>\u003Cp style='box-sizing: border-box;margin: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;text-indent: 0px;padding: 8px 0px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-style: normal;font-variant-ligatures: normal;font-variant-caps: normal;font-weight: 400;orphans: 2;text-transform: none;widows: 2;word-spacing: 0px;-webkit-text-stroke-width: 0px;white-space: normal;background-color: rgb(255, 255, 255);text-decoration-thickness: initial;text-decoration-style: initial;text-decoration-color: initial;'>\u003Cspan leaf=\"\">The DeepSeek-R1 paper demonstrates the effectiveness of applying reinforcement learning (RL) to language models, especially in solving math problems. In the early stages of training, the model performed poorly when solving basic math problems, but with thousands of optimization steps during the RL process, its accuracy significantly improved. Notably, it wasn't just the quantitative improvement in model accuracy that stood out, but more importantly, the qualitative change in its problem-solving methods.\u003C/span>\u003C/p>\u003Csection style=\"text-align: center;\" nodeleaf=\"\">\u003Cimg class=\"rich_pages wxw-img js_insertlocalimg\" data-ratio=\"0.6114494518879415\" data-s=\"300,640\" data-type=\"png\" data-w=\"821\" type=\"block\" data-imgfileid=\"100010242\" src=\"./assets/17423769993450.12758870708800973.png\">\u003C/section>\u003Cp style='box-sizing: border-box;margin: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;text-indent: 0px;padding: 8px 0px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-style: normal;font-variant-ligatures: normal;font-variant-caps: normal;font-weight: 400;orphans: 2;text-transform: none;widows: 2;word-spacing: 0px;-webkit-text-stroke-width: 0px;white-space: normal;background-color: rgb(255, 255, 255);text-decoration-thickness: initial;text-decoration-style: initial;text-decoration-color: initial;'>\u003Cspan leaf=\"\">As the model was optimized, a significant phenomenon was observed: the model began to generate longer answers. This increase in response length stemmed from the model discovering that more detailed solutions could improve accuracy. It learned to \"re-evaluate\" steps, backtrack its own thinking, and re-examine problems from different angles. For example, it might say, \"Wait, let me re-check step by step to confirm the correct sum.\"\u003C/span>\u003C/p>\u003Csection style=\"text-align: center;\" nodeleaf=\"\">\u003Cimg class=\"rich_pages wxw-img js_insertlocalimg\" data-ratio=\"0.6\" data-s=\"300,640\" data-type=\"png\" data-w=\"960\" type=\"block\" data-imgfileid=\"100010243\" src=\"./assets/17423769993410.7536635630613766.png\">\u003C/section>\u003Cp style='box-sizing: border-box;margin: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;text-indent: 0px;padding: 8px 0px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-style: normal;font-variant-ligatures: normal;font-variant-caps: normal;font-weight: 400;orphans: 2;text-transform: none;widows: 2;word-spacing: 0px;-webkit-text-stroke-width: 0px;white-space: normal;background-color: rgb(255, 255, 255);text-decoration-thickness: initial;text-decoration-style: initial;text-decoration-color: initial;'>\u003Cspan leaf=\"\">This process is highly similar to how humans solve problems: backtracking, trying different approaches, and gradually refining solutions. Through the RL process, these cognitive strategies naturally emerged. More interestingly, this problem-solving strategy cannot be hard-coded into the model but is gradually discovered through trial and error and learning during the RL optimization process. The only external guidance the model receives is the correct answer.\u003C/span>\u003C/p>\u003Cp style='box-sizing: border-box;margin: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;text-indent: 0px;padding: 8px 0px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-style: normal;font-variant-ligatures: normal;font-variant-caps: normal;font-weight: 400;orphans: 2;text-transform: none;widows: 2;word-spacing: 0px;-webkit-text-stroke-width: 0px;white-space: normal;background-color: rgb(255, 255, 255);text-decoration-thickness: initial;text-decoration-style: initial;text-decoration-color: initial;'>\u003Cspan leaf=\"\">The most astonishing aspect is that the model learned to think and developed strategies akin to human cognition, all without explicit programming but rather spontaneously emerging during the reinforcement learning optimization process. This is a cognitive strategy used to \"manipulate\" problems, understand them from different perspectives, or solve them using analogies. The discovery of this \"chain of thought\" is a direct result of the RL optimization process, showcasing the power and spontaneity of this method.\u003C/span>\u003C/p>\u003Cblockquote style='box-sizing: border-box;margin: 20px 0px;cursor: pointer;padding: 10px 10px 10px 20px;border-style: none none none solid;border-width: 3px;border-color: rgba(0, 0, 0, 0.4);border-radius: 0px;background: none 0% 0% / auto no-repeat scroll padding-box border-box rgba(0, 0, 0, 0.05);width: auto;height: auto;box-shadow: rgba(0, 0, 0, 0) 0px 0px 0px 0px;display: block;overflow: auto;color: rgb(0, 0, 0);font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-size: 16px;font-style: normal;font-variant-ligatures: normal;font-variant-caps: normal;font-weight: 400;letter-spacing: normal;orphans: 2;text-align: left;text-indent: 0px;text-transform: none;widows: 2;word-spacing: 0px;-webkit-text-stroke-width: 0px;white-space: normal;text-decoration-thickness: initial;text-decoration-style: initial;text-decoration-color: initial;'>\u003Cp style=\"box-sizing: border-box;margin: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: 0em;text-align: left;text-indent: 0em;padding: 8px 0px;font-weight: normal;\">\u003Cspan leaf=\"\">Emily buys 3 apples and 2 oranges. Each orange costs 2 Dollars. The total cost of all the fruit is 13 Dollars. What is the cost of apples?\u003C/span>\u003C/p>\u003C/blockquote>\u003Cp style='box-sizing: border-box;margin: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;text-indent: 0px;padding: 8px 0px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-style: normal;font-variant-ligatures: normal;font-variant-caps: normal;font-weight: 400;orphans: 2;text-transform: none;widows: 2;word-spacing: 0px;-webkit-text-stroke-width: 0px;white-space: normal;background-color: rgb(255, 255, 255);text-decoration-thickness: initial;text-decoration-style: initial;text-decoration-color: initial;'>\u003Cspan leaf=\"\">ChatGPT 4o received an answer like this. What is shown here are the results we obtained when using the basic SFT (Supervised Fine-Tuning) method previously, which is akin to mimicking an expert's solution.\u003C/span>\u003C/p>\u003Csection style=\"text-align: center;\" nodeleaf=\"\">\u003Cimg class=\"rich_pages wxw-img js_insertlocalimg\" data-ratio=\"0.782608695652174\" data-s=\"300,640\" data-type=\"png\" data-w=\"966\" type=\"block\" data-imgfileid=\"100010244\" src=\"./assets/17423769993420.2888606000330005.png\">\u003C/section>\u003Cp style='box-sizing: border-box;margin: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;text-indent: 0px;padding: 8px 0px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-style: normal;font-variant-ligatures: normal;font-variant-caps: normal;font-weight: 400;orphans: 2;text-transform: none;widows: 2;word-spacing: 0px;-webkit-text-stroke-width: 0px;white-space: normal;background-color: rgb(255, 255, 255);text-decoration-thickness: initial;text-decoration-style: initial;text-decoration-color: initial;'>\u003Cspan leaf=\"\">When the same question is given to so-called reasoning or thinking models, the output is as follows. These are the results we obtained from Reinforcement Learning (RL) models.\u003C/span>\u003C/p>\u003Csection style=\"text-align: center;\" nodeleaf=\"\">\u003Cimg class=\"rich_pages wxw-img js_insertlocalimg\" data-ratio=\"0.65\" data-s=\"300,640\" data-type=\"png\" data-w=\"1080\" type=\"block\" data-imgfileid=\"100010245\" src=\"./assets/17423770005220.2888051664604683.png\">\u003C/section>\u003Cp style='box-sizing: border-box;margin: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;text-indent: 0px;padding: 8px 0px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-style: normal;font-variant-ligatures: normal;font-variant-caps: normal;font-weight: 400;orphans: 2;text-transform: none;widows: 2;word-spacing: 0px;-webkit-text-stroke-width: 0px;white-space: normal;background-color: rgb(255, 255, 255);text-decoration-thickness: initial;text-decoration-style: initial;text-decoration-color: initial;'>\u003Cspan leaf=\"\">As you read through this process, you can't help but feel like the model is thinking; it's clearly pursuing a solution. It guesses that the answer must be $3, then it says, \"Wait, let me check my math again, just to confirm.\" Then it tries again from a slightly different angle, and it says, \"Yes, everything checks out, I think this is the answer, I don't see any mistakes. Let me see if there's another way to solve this problem, maybe I can set up an equation.\" For example, \"Let's assume the price of an apple is $8, then... wait, no, the answer is the same. So, each apple is indeed $3, good, I'm confident this is correct.\"\u003C/span>\u003C/p>\u003Csection style=\"text-align: center;\" nodeleaf=\"\">\u003Cimg class=\"rich_pages wxw-img js_insertlocalimg\" data-ratio=\"0.6018518518518519\" data-s=\"300,640\" data-type=\"png\" data-w=\"1080\" type=\"block\" data-imgfileid=\"100010246\" src=\"./assets/17423769993450.7706828315815406.png\">\u003C/section>\u003Cp style='box-sizing: border-box;margin: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;text-indent: 0px;padding: 8px 0px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-style: normal;font-variant-ligatures: normal;font-variant-caps: normal;font-weight: 400;orphans: 2;text-transform: none;widows: 2;word-spacing: 0px;-webkit-text-stroke-width: 0px;white-space: normal;background-color: rgb(255, 255, 255);text-decoration-thickness: initial;text-decoration-style: initial;text-decoration-color: initial;'>\u003Cspan leaf=\"\">Then, after the model completes its thought process, it writes out a beautiful solution for humans. So, it's not just about correctness, but also about presentation; it writes the solution very clearly and boxes the correct answer at the bottom.\u003C/span>\u003C/p>\u003Csection style=\"text-align: center;\" nodeleaf=\"\">\u003Cimg class=\"rich_pages wxw-img js_insertlocalimg\" data-ratio=\"0.3474903474903475\" data-s=\"300,640\" data-type=\"png\" data-w=\"1036\" style=\"width:578px;height:164px;\" type=\"block\" data-croporisrc=\"https://mmbiz.qpic.cn/sz_mmbiz_png/YdtkbCEBMDFriba81koRuiccGwzNN0SKQUlf6zhVZDlaTEfTias2Peo9ic6q8XmfKTL4spbvvzDhxEsjVzDrkj1GJA/640?wx_fmt=png&from=appmsg\" data-cropx2=\"1036\" data-cropy2=\"293.9515570934256\" data-imgfileid=\"100010247\" src=\"./assets/17423769996420.7806282746008522.jpeg\">\u003C/section>\u003Cp style='box-sizing: border-box;margin: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;text-indent: 0px;padding: 8px 0px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-style: normal;font-variant-ligatures: normal;font-variant-caps: normal;font-weight: 400;orphans: 2;text-transform: none;widows: 2;word-spacing: 0px;-webkit-text-stroke-width: 0px;white-space: normal;background-color: rgb(255, 255, 255);text-decoration-thickness: initial;text-decoration-style: initial;text-decoration-color: initial;'>\u003Cspan leaf=\"\">Incredibly, we can see this thought process in the model, and it comes directly from the reinforcement learning process. This is why, as the token sequence gets longer, they are thinking and trying different approaches.\u003C/span>\u003C/p>\u003Csection style=\"text-align: center;\" nodeleaf=\"\">\u003Cimg class=\"rich_pages wxw-img js_insertlocalimg\" data-ratio=\"0.6493184634448576\" data-s=\"300,640\" data-type=\"png\" data-w=\"807\" type=\"block\" data-imgfileid=\"100010249\" src=\"./assets/17423769994000.5791509680594886.png\">\u003C/section>\u003Cp style='box-sizing: border-box;margin: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;text-indent: 0px;padding: 8px 0px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-style: normal;font-variant-ligatures: normal;font-variant-caps: normal;font-weight: 400;orphans: 2;text-transform: none;widows: 2;word-spacing: 0px;-webkit-text-stroke-width: 0px;white-space: normal;background-color: rgb(255, 255, 255);text-decoration-thickness: initial;text-decoration-style: initial;text-decoration-color: initial;'>\u003Cspan leaf=\"\">This is also why accuracy improves when solving problems. What we see here are those \"Aha\" moments, different strategies, and some thoughts on how to ensure the right answer is obtained.\u003C/span>\u003C/p>\u003Cp style='box-sizing: border-box;margin: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;text-indent: 0px;padding: 8px 0px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-style: normal;font-variant-ligatures: normal;font-variant-caps: normal;font-weight: 400;orphans: 2;text-transform: none;widows: 2;word-spacing: 0px;-webkit-text-stroke-width: 0px;white-space: normal;background-color: rgb(255, 255, 255);text-decoration-thickness: initial;text-decoration-style: initial;text-decoration-color: initial;'>\u003Cspan leaf=\"\">ChatGPT, some of them like o1, o3-mini, o3-mini-high, etc., use advanced reasoning techniques. The phrase \"use advanced reasoning\" means they are trained with reinforcement learning. Models like GPT-4 or GPT-4o mini, what you get in the free version should be considered mainly SFT models (supervised fine-tuning models); they don't actually think in the way RL models do. Although there is some reinforcement learning involved in these models, most of them are still SFT models.\u003C/span>\u003C/p>\u003Csection style=\"text-align: center;\" nodeleaf=\"\">\u003Cimg class=\"rich_pages wxw-img js_insertlocalimg\" data-ratio=\"1.4893617021276595\" data-s=\"300,640\" data-type=\"png\" data-w=\"423\" style=\"width:326px;height:486px;\" type=\"block\" data-imgfileid=\"100010250\" src=\"./assets/17423770002130.8843294206726133.png\">\u003C/section>\u003Cp style='box-sizing: border-box;margin: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;text-indent: 0px;padding: 8px 0px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-style: normal;font-variant-ligatures: normal;font-variant-caps: normal;font-weight: 400;orphans: 2;text-transform: none;widows: 2;word-spacing: 0px;-webkit-text-stroke-width: 0px;white-space: normal;background-color: rgb(255, 255, 255);text-decoration-thickness: initial;text-decoration-style: initial;text-decoration-color: initial;'>\u003Cspan leaf=\"\">Although the model generates these chains of thought in the background, OpenAI chooses not to display the detailed content of these chains of thought on the web interface but instead shows summaries of these chains. Part of the reason OpenAI does this is that they are concerned about so-called \"distillation risks,\" meaning someone might attempt to restore reasoning performance by simply imitating these traces of reasoning. Therefore, they hide the detailed content and only show the summary. As a result, you won't get a complete reasoning process like with DeepSeek.\u003C/span>\u003C/p>\u003Csection style=\"text-align: center;\" nodeleaf=\"\">\u003Cimg class=\"rich_pages wxw-img js_insertlocalimg\" data-ratio=\"0.37156398104265403\" data-s=\"300,640\" data-type=\"png\" data-w=\"1055\" type=\"block\" data-imgfileid=\"100010251\" src=\"./assets/17423769998600.9995544759851314.png\">\u003C/section>\u003Cp style='box-sizing: border-box;margin: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;text-indent: 0px;padding: 8px 0px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-style: normal;font-variant-ligatures: normal;font-variant-caps: normal;font-weight: 400;orphans: 2;text-transform: none;widows: 2;word-spacing: 0px;-webkit-text-stroke-width: 0px;white-space: normal;background-color: rgb(255, 255, 255);text-decoration-thickness: initial;text-decoration-style: initial;text-decoration-color: initial;'>\u003Cspan leaf=\"\">Then the model will write out the solution. So, even though we don't see all the backend details of these models, their performances are roughly equivalent.\u003C/span>\u003C/p>\u003Csection style=\"text-align: center;\" nodeleaf=\"\">\u003Cimg class=\"rich_pages wxw-img js_insertlocalimg\" data-ratio=\"0.5594886922320551\" data-s=\"300,640\" data-type=\"png\" data-w=\"1017\" style=\"width:578px;height:224px;\" type=\"block\" data-croporisrc=\"https://mmbiz.qpic.cn/sz_mmbiz_png/YdtkbCEBMDFriba81koRuiccGwzNN0SKQUibibBjwnYxD8VT3kEprseuBAicSdLRGVmmaJXt27vLL96gjic3Dic6tHmnA/640?wx_fmt=png&from=appmsg\" data-cropx2=\"1017\" data-cropy2=\"394.1314878892734\" data-imgfileid=\"100010252\" src=\"./assets/17423769993420.8676378099419007.jpeg\">\u003C/section>\u003Cp style='box-sizing: border-box;margin: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;text-indent: 0px;padding: 8px 0px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-style: normal;font-variant-ligatures: normal;font-variant-caps: normal;font-weight: 400;orphans: 2;text-transform: none;widows: 2;word-spacing: 0px;-webkit-text-stroke-width: 0px;white-space: normal;background-color: rgb(255, 255, 255);text-decoration-thickness: initial;text-decoration-style: initial;text-decoration-color: initial;'>\u003Cspan leaf=\"\">If you have questions requiring advanced reasoning, you may want to use some thinking models. In many simple cases, such as knowledge-based questions or similar ones, using a thinking model might be excessive; there's no need to think for 30 seconds for a factual question. Therefore, 80%-90% of the time, GPT-4 alone suffices, and when encountering more difficult problems like math or programming, you can choose the thinking model, but then you'll have to wait longer because they need time to think.\u003C/span>\u003C/p>\u003Cp style='box-sizing: border-box;margin: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;text-indent: 0px;padding: 8px 0px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-style: normal;font-variant-ligatures: normal;font-variant-caps: normal;font-weight: 400;orphans: 2;text-transform: none;widows: 2;word-spacing: 0px;-webkit-text-stroke-width: 0px;white-space: normal;background-color: rgb(255, 255, 255);text-decoration-thickness: initial;text-decoration-style: initial;text-decoration-color: initial;'>\u003Cspan leaf=\"\">The Gemini 2.0 Flash Thinking Experimental in Google AI Studio can also be tried—it’s an early experimental thinking model from Google. We can input the same question here and click run; it's also a thinking model, and it will provide the correct answer. Basically, Gemini also offers a thinking model, while Anthropic currently does not, but this represents the cutting-edge development of these LLMs.\u003C/span>\u003C/p>\u003Csection style=\"text-align: center;\" nodeleaf=\"\">\u003Cimg class=\"rich_pages wxw-img js_insertlocalimg\" data-ratio=\"0.6737247353224254\" data-s=\"300,640\" data-type=\"png\" data-w=\"1039\" type=\"block\" data-imgfileid=\"100010253\" src=\"./assets/17423770012810.2871891120615191.png\">\u003C/section>\u003Cp style='box-sizing: border-box;margin: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;text-indent: 0px;padding: 8px 0px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-style: normal;font-variant-ligatures: normal;font-variant-caps: normal;font-weight: 400;orphans: 2;text-transform: none;widows: 2;word-spacing: 0px;-webkit-text-stroke-width: 0px;white-space: normal;background-color: rgb(255, 255, 255);text-decoration-thickness: initial;text-decoration-style: initial;text-decoration-color: initial;'>\u003Cspan leaf=\"\">RL is like an emerging and exciting phase, but achieving accuracy in detail is challenging, which is why these models and thinking models remain experimental. By early 2025, they will still be in a very early stage. But this is the frontier area driving the performance improvement of these extremely difficult problems, utilizing the reasoning that emerges in these optimizations.\u003C/span>\u003C/p>\u003Cp style=\"display: none;\">\u003Cmp-style-type data-value=\"3\">\u003C/mp-style-type>\u003C/p>\u003C/div>",[257,266,273,281,289,297,305,312],{"id":258,"title_md5":259,"publish_date":260,"author_md5":261,"is_original":23,"collection":262,"summary_md5":263,"cover_url":264,"cover_url_1_1":265},264,"aa59bd591f99fffe5f2a597ba9b78001","2024-05-26","bc27fa490c4d0d525bac812fc0793534","#Google #DeepMind #Voe #Imagen3","84077d2339dc73b229e3dd8bfc829797","article_res/cover/cec3437d945bc384856e90bb19de83f4.jpeg","article_res/cover/3b1e6608a6260acbe0d48507dea22e26.jpeg",{"id":267,"title_md5":268,"publish_date":269,"author_md5":261,"is_original":4,"collection":65,"summary_md5":270,"cover_url":271,"cover_url_1_1":272},423,"353c79fd0dfd839c493fd1f120759e3f","2023-08-08","0ec8feda670e2d0b61eba8192e0df1be","article_res/cover/969d465ab83c09548af661cf27d35200.jpeg","article_res/cover/07a105def6623dfafe9f093a7d0167e7.jpeg",{"id":274,"title_md5":275,"publish_date":276,"author_md5":261,"is_original":23,"collection":277,"summary_md5":278,"cover_url":279,"cover_url_1_1":280},23,"8d46ceaf1e85f871c3c3344fd009f823","2025-02-28","#OpenAI #ChatGPT #GPT-4.5","131adbbbd27fd4616e22cc9634533d27","article_res/cover/9924bb850ad29e32300b9374df44a7b4.jpeg","article_res/cover/f820c98591983b4e2096c50073326c7f.jpeg",{"id":282,"title_md5":283,"publish_date":284,"author_md5":261,"is_original":23,"collection":285,"summary_md5":286,"cover_url":287,"cover_url_1_1":288},170,"3c2282826e9a5f0fc6335c09b7e5b8e9","2024-09-23","#History of Intelligence #Neuroscience","965afaed0e5319ac56fb790dc20ab51d","article_res/cover/2358dbafe8f220d1f5a7ead2acc9e8a7.jpeg","article_res/cover/21760dac6e7ffdb811f9b3e61840f7aa.jpeg",{"id":290,"title_md5":291,"publish_date":292,"author_md5":261,"is_original":23,"collection":293,"summary_md5":294,"cover_url":295,"cover_url_1_1":296},104,"8791197cf25d34e57ca485d200f7b491","2024-12-11","#OpenAI #LLM","a8b9894f48377eb8e4598e8b197b15a5","article_res/cover/a1eab8378bbd7ab772030cc380ceb5fa.jpeg","article_res/cover/f880ea0d072555de14429c456cbc05e8.jpeg",{"id":298,"title_md5":299,"publish_date":300,"author_md5":301,"is_original":4,"collection":5,"summary_md5":302,"cover_url":303,"cover_url_1_1":304},566,"c595e3a12dab25c053b5bb15e3e1a463","2022-04-20","8b3607d0f4181a3cb6ffdccf7185f09b","8dd1a03a8286151a4ff2e45479dd39bd","article_res/cover/2d329b29aa46690ba8269e5d7a33c279.jpeg","article_res/cover/028d5977258f985de33693fdb192737f.jpeg",{"id":306,"title_md5":307,"publish_date":308,"author_md5":301,"is_original":4,"collection":5,"summary_md5":309,"cover_url":310,"cover_url_1_1":311},549,"a2facca5fd6832e9581cd31dff0c0230","2022-05-07","2f0b57ca9b630b0fb7dd10ded65a4baa","article_res/cover/1eea60ce387b2369e138dff8f0f787cb.jpeg","article_res/cover/eae5ec64a31f3e6e8547fd987fb9e861.jpeg",{"id":313,"title_md5":314,"publish_date":315,"author_md5":261,"is_original":23,"collection":316,"summary_md5":317,"cover_url":318,"cover_url_1_1":319},119,"526d0f374ee04f855666789603e8bdf8","2024-11-25","#AI Image Generator","f3521bc722ed915ad6e32225f2baf710","article_res/cover/621440bd2cd3230cd8481c4ed44378f1.jpeg","article_res/cover/177d7b243e807ca843f41ac82be538e1.jpeg",{"related":321,"small":355},[322,323,331,339,347],{"id":227,"publish_date":228,"is_original":23,"collection":229,"cover_url":230,"cover_url_1_1":231,"title":232,"summary":233,"author":28},{"id":324,"publish_date":325,"is_original":23,"collection":326,"cover_url":327,"cover_url_1_1":328,"title":329,"summary":330,"author":28},73,"2025-01-10","#Adobe #AI 3D Generator #AI Avatar","article_res/cover/9d7456066a304f8b37cf01efa6a57103.jpeg","article_res/cover/69949cc4407fbfe3ca0d5d92d21fd597.jpeg","Adobe's FaceLift - 3D Head Reconstruction Tool from a Single Image","FaceLift: Single Image to 3D Head with View Generation and GS-LRM",{"id":332,"publish_date":333,"is_original":23,"collection":334,"cover_url":335,"cover_url_1_1":336,"title":337,"summary":338,"author":28},50,"2025-02-03","#OpenAI #LLM #AI Agent #AI Research #RL","article_res/cover/01d12853914b4549d0556b990ccbfb1b.jpeg","article_res/cover/7260f24c0367b431ad9245c97db8d4c6.jpeg","OpenAI Deep Research: Intelligent research assistant launched","An agent that uses reasoning to synthesize large amounts of online information and complete multi-step research tasks.",{"id":340,"publish_date":341,"is_original":23,"collection":342,"cover_url":343,"cover_url_1_1":344,"title":345,"summary":346,"author":28},90,"2024-12-25","#AI Agent #Microsoft #Anthropic #LLM #Langchain","article_res/cover/6e5c663691d0ab5cd19bc6dbed6bc0f1.jpeg","article_res/cover/271445d9bce34ec6eff52b1fc876576d.jpeg","Building AI Agents","The road to AGI",{"id":348,"publish_date":349,"is_original":23,"collection":350,"cover_url":351,"cover_url_1_1":352,"title":353,"summary":354,"author":28},152,"2024-10-14","#Anthropic","article_res/cover/1d0d0b428448fa9d59e317095a14638d.jpeg","article_res/cover/cd8a0531398afce679bf0950fc2ec7d2.jpeg","Part 2 of Anthropic CEO Dario's AI article - 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