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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":320,"category":376,"tag":377},81,"2025-01-03","#Google #DeepMind #LLM","M_EluHNM1g7HcCqliYsxTQ","article_res/cover/aca6c222beea574e1b5d56e3ed173755.jpeg","article_res/cover/0348e2bd18357219dc2f74173b968785.jpeg","Google Deepmind's AI Self-Improvement Learning - Socratic Learning Through Language Games","From \"Attention is All You Need\" to \"Language Games Are All You Need\"","\u003Cdiv class=\"rich_media_content js_underline_content\n                       autoTypeSetting24psection\n            \" id=\"js_content\">\u003Cp style='margin-bottom: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;padding-top: 8px;padding-bottom: 8px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;background-color: rgb(255, 255, 255);'>, which mentioned AI's self-improvement and Automating AI research. Today, I came across an article by Google Deepmind: 《Boundless Socratic Learning with Language Games》, which proposes a unique concept for \"self-improving learning within a closed-loop system.\" This kind of \"Socratic Learning\" pushes the potential of artificial intelligence to its limits through recursive improvements that match input and output spaces.\u003C/p>\u003Ch2 style='margin-top: 30px;margin-bottom: 15px;color: rgba(0, 0, 0, 0.85);cursor: pointer;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;letter-spacing: normal;text-align: left;background-color: rgb(255, 255, 255);'>\u003Cspan style=\"cursor: pointer;font-size: 22px;color: rgb(0, 0, 0);line-height: 1.5em;letter-spacing: 0em;font-weight: bold;display: block;\">Definition\u003C/span>\u003C/h2>\u003Cp style=\"text-align: center;\">\u003Cimg class=\"rich_pages wxw-img js_insertlocalimg\" data-imgfileid=\"100008779\" data-ratio=\"0.41944444444444445\" data-s=\"300,640\" data-type=\"png\" data-w=\"1080\" style=\"\" src=\"./assets/17423772279040.359362271891966.png\">\u003C/p>\u003Cp style='margin-bottom: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;padding-top: 8px;padding-bottom: 8px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;background-color: rgb(255, 255, 255);'>In a closed system (black), the red agent improves itself through language interaction. External observers (gold) evaluate the agent's performance through performance metrics (green). If the agent's output can influence future inputs (blue path) and performance metrics continue to improve, it can be called self-improvement. If the agent's input and output space are compatible, this self-improvement is recursive; if the input and output space is language, it is called \"Socratic Learning.\"\u003C/p>\u003Ch2 style='margin-top: 30px;margin-bottom: 15px;color: rgba(0, 0, 0, 0.85);cursor: pointer;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;letter-spacing: normal;text-align: left;background-color: rgb(255, 255, 255);'>\u003Cspan style=\"cursor: pointer;font-size: 22px;color: rgb(0, 0, 0);line-height: 1.5em;letter-spacing: 0em;font-weight: bold;display: block;\">Self-improvement\u003C/span>\u003C/h2>\u003Cp style='margin-bottom: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;padding-top: 8px;padding-bottom: 8px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;background-color: rgb(255, 255, 255);'>Self-improvement is a special enhancement process whose core lies in the fact that the agent's output (i.e., its behavior) will, in turn, affect its future learning. This means the agent can shape its own experience flow, achieving potentially infinite improvement within a closed system.\u003C/p>\u003Cp style='margin-bottom: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;padding-top: 8px;padding-bottom: 8px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;background-color: rgb(255, 255, 255);'>, where the agent plays both the player and opponent roles in a symmetric game, generating streams of experience with feedback (such as \"who won?\") to continuously enhance skills.\u003C/p>\u003Cp style='margin-bottom: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;padding-top: 8px;padding-bottom: 8px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;background-color: rgb(255, 255, 255);'>— relates to the practicality of implementation.\u003C/p>\u003Ch2 style='margin-top: 30px;margin-bottom: 15px;color: rgba(0, 0, 0, 0.85);cursor: pointer;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;letter-spacing: normal;text-align: left;background-color: rgb(255, 255, 255);'>\u003Cspan style=\"cursor: pointer;font-size: 22px;color: rgb(0, 0, 0);line-height: 1.5em;letter-spacing: 0em;font-weight: bold;display: block;\">Socratic Learning\u003C/span>\u003C/h2>\u003Cp style='margin-bottom: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;padding-top: 8px;padding-bottom: 8px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;background-color: rgb(255, 255, 255);'>The self-improvement we discuss here is a recursive form, requiring compatibility between the agent's input and output (i.e., they exist in the same space). Under this mode, the agent's output becomes future input, forming a closed loop.\u003C/p>\u003Cp style='margin-bottom: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;padding-top: 8px;padding-bottom: 8px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;background-color: rgb(255, 255, 255);'>It should be noted that the input and output spaces are not entirely identical but partially overlapping. For instance, the agent might generate code while perceiving natural language, (partially self-generated) code, and execution traces. This form is more restrictive than general self-improvement because it reduces the mediating role of the environment — typically, the agent's output only indirectly affects its input distribution through complex environmental mappings. Recursive self-improvement, on the other hand, directly depends on the compatibility between output and input.\u003C/p>\u003Cp style='margin-bottom: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;padding-top: 8px;padding-bottom: 8px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;background-color: rgb(255, 255, 255);'>This recursive mechanism is one property of many open-ended evolutionary processes, and open-ended improvement is considered a core feature leading to artificial superintelligence (ASI). Despite this, this compatibility is less stringent than traditional homomorphic self-modification, making it more universally applicable.\u003C/p>\u003Ch2 style='margin-top: 30px;margin-bottom: 15px;color: rgba(0, 0, 0, 0.85);cursor: pointer;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;letter-spacing: normal;text-align: left;background-color: rgb(255, 255, 255);'>\u003Cspan style=\"cursor: pointer;font-size: 22px;color: rgb(0, 0, 0);line-height: 1.5em;letter-spacing: 0em;font-weight: bold;display: block;\">Language games\u003C/span>\u003C/h2>\u003Cp style='margin-bottom: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;padding-top: 8px;padding-bottom: 8px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;background-color: rgb(255, 255, 255);'>From “Attention is All You Need” to “Language Games Are All You Need.”\u003C/p>\u003Cp style='margin-bottom: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;padding-top: 8px;padding-bottom: 8px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;background-color: rgb(255, 255, 255);'>concept provides significant inspiration for this field. He proposed in his Philosophical Investigations: “Language and the actions it interweaves form a whole, which I call ‘language games.’”\u003C/p>\u003Cp style='margin-bottom: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;padding-top: 8px;padding-bottom: 8px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;background-color: rgb(255, 255, 255);'>Wittgenstein believed that the meaning of language does not lie in the words themselves but is embodied in the process of language interaction. Inspired by this, this paper defines “language games” as an interactive protocol (a set of rules representable in code) that specifies the way interactions based on linguistic inputs and outputs occur among one or more agents (“players”), and at the end of the game, provides each player with a scalar scoring function.\u003C/p>\u003Cp style='margin-bottom: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;padding-top: 8px;padding-bottom: 8px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;background-color: rgb(255, 255, 255);'>It is important to emphasize that, to ensure operability, we assume all language games can terminate within finite time. This definition not only tightly integrates language with action but also provides a theoretical framework for building efficient collaboration among agents.\u003C/p>\u003Ch2 style='margin-top: 30px;margin-bottom: 15px;color: rgba(0, 0, 0, 0.85);cursor: pointer;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;letter-spacing: normal;text-align: left;background-color: rgb(255, 255, 255);'>\u003Cspan style=\"cursor: pointer;font-size: 22px;color: rgb(0, 0, 0);line-height: 1.5em;letter-spacing: 0em;font-weight: bold;display: block;\">Language games: meeting the core needs of Socratic learning\u003C/span>\u003C/h2>\u003Cp style='margin-bottom: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;padding-top: 8px;padding-bottom: 8px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;background-color: rgb(255, 255, 255);'>Language games (Language Games), by their definition, precisely meet the two core needs of Socratic learning:\u003C/p>\u003Col style='margin-top: 8px;margin-bottom: 8px;cursor: pointer;padding-left: 25px;color: rgb(0, 0, 0);font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-size: 16px;letter-spacing: normal;text-align: left;background-color: rgb(255, 255, 255);' class=\"list-paddingleft-1\">\u003Cli style=\"cursor: pointer;\">\u003Csection style=\"cursor: pointer;margin-top: 5px;margin-bottom: 5px;color: rgb(1, 1, 1);line-height: 1.8em;letter-spacing: 0em;\">Providing scalable mechanisms for generating interactive data (including self-play);\u003C/section>\u003C/li>\u003Cli style=\"cursor: pointer;\">\u003Csection style=\"cursor: pointer;margin-top: 5px;margin-bottom: 5px;color: rgb(1, 1, 1);line-height: 1.8em;letter-spacing: 0em;\">Automatically generating accompanying feedback signals (scores).\u003C/section>\u003C/li>\u003C/ol>\u003Cp style='margin-bottom: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;padding-top: 8px;padding-bottom: 8px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;background-color: rgb(255, 255, 255);'>In fact, this is almost the logical necessity of coverage and feedback conditions: all operational forms of interactive data generation with feedback signals are essentially language games. Furthermore, this definition allows for the introduction of rich strategic diversity brought by multi-agent dynamics, better satisfying the coverage condition. Moreover, this multi-agent collaboration is closer to the dynamic social co-construction of philosophers rather than just the soliloquy of a \"loner who has lived for a thousand years.\"\u003C/p>\u003Cp style='margin-bottom: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;padding-top: 8px;padding-bottom: 8px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;background-color: rgb(255, 255, 255);'>From a practical standpoint, language games are easier to get started with. Human history has already created and refined a wide variety of games and player skills, providing a rich empirical basis for constructing language games. Nguyen even views this diversity as a reflection of human agency and local motivational flexibility. Some theorists (like Derrida) might further argue that any discourse, in a sense, already possesses the structure of a game. As Derrida said: \"Every discourse, even poetry or oracles, contains a rule system that generates similar things, thereby outlining a methodology.\"\u003C/p>\u003Cp style='margin-bottom: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;padding-top: 8px;padding-bottom: 8px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;background-color: rgb(255, 255, 255);'>Related concepts can be traced back to Vygotsky's autotelic agents. Colas et al. believe that even if these agents are not closed-loop systems, many of their \"internalized social interactions\" can still be regarded as language games. Many common large language model (LLM) interaction paradigms can also be well represented as language games, such as:\u003C/p>\u003Cul style='margin-top: 8px;margin-bottom: 8px;cursor: pointer;padding-left: 25px;color: rgb(0, 0, 0);font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-size: 16px;letter-spacing: normal;text-align: left;background-color: rgb(255, 255, 255);' class=\"list-paddingleft-1\">\u003Cli style=\"cursor: pointer;\">\u003Csection style=\"cursor: pointer;margin-top: 5px;margin-bottom: 5px;color: rgb(1, 1, 1);line-height: 1.8em;letter-spacing: 0em;\">\u003Cstrong style=\"cursor: pointer;color: rgb(0, 0, 0);background: none 0% 0% / auto no-repeat scroll padding-box border-box rgba(0, 0, 0, 0);width: auto;height: auto;border-style: none;border-width: 3px;border-color: rgba(0, 0, 0, 0.4);border-radius: 0px;\">Debate\u003C/strong>\u003C/section>\u003C/li>\u003Cli style=\"cursor: pointer;\">\u003Csection style=\"cursor: pointer;margin-top: 5px;margin-bottom: 5px;color: rgb(1, 1, 1);line-height: 1.8em;letter-spacing: 0em;\">\u003Cstrong style=\"cursor: pointer;color: rgb(0, 0, 0);background: none 0% 0% / auto no-repeat scroll padding-box border-box rgba(0, 0, 0, 0);width: auto;height: auto;border-style: none;border-width: 3px;border-color: rgba(0, 0, 0, 0.4);border-radius: 0px;\">Role-playing\u003C/strong>\u003C/section>\u003C/li>\u003Cli style=\"cursor: pointer;\">\u003Csection style=\"cursor: pointer;margin-top: 5px;margin-bottom: 5px;color: rgb(1, 1, 1);line-height: 1.8em;letter-spacing: 0em;\">\u003Cstrong style=\"cursor: pointer;color: rgb(0, 0, 0);background: none 0% 0% / auto no-repeat scroll padding-box border-box rgba(0, 0, 0, 0);width: auto;height: auto;border-style: none;border-width: 3px;border-color: rgba(0, 0, 0, 0.4);border-radius: 0px;\">Theory of mind\u003C/strong>\u003C/section>\u003C/li>\u003Cli style=\"cursor: pointer;\">\u003Csection style=\"cursor: pointer;margin-top: 5px;margin-bottom: 5px;color: rgb(1, 1, 1);line-height: 1.8em;letter-spacing: 0em;\">\u003Cstrong style=\"cursor: pointer;color: rgb(0, 0, 0);background: none 0% 0% / auto no-repeat scroll padding-box border-box rgba(0, 0, 0, 0);width: auto;height: auto;border-style: none;border-width: 3px;border-color: rgba(0, 0, 0, 0.4);border-radius: 0px;\">Negotiation\u003C/strong>\u003C/section>\u003C/li>\u003Cli style=\"cursor: pointer;\">\u003Csection style=\"cursor: pointer;margin-top: 5px;margin-bottom: 5px;color: rgb(1, 1, 1);line-height: 1.8em;letter-spacing: 0em;\">\u003Cstrong style=\"cursor: pointer;color: rgb(0, 0, 0);background: none 0% 0% / auto no-repeat scroll padding-box border-box rgba(0, 0, 0, 0);width: auto;height: auto;border-style: none;border-width: 3px;border-color: rgba(0, 0, 0, 0.4);border-radius: 0px;\">Jailbreak defense\u003C/strong>\u003C/section>\u003C/li>\u003C/ul>\u003Cp style='margin-bottom: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;padding-top: 8px;padding-bottom: 8px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;background-color: rgb(255, 255, 255);'>Even in open systems, reinforcement learning based on human feedback can fall into this category.\u003C/p>\u003Ch2 style='margin-top: 30px;margin-bottom: 15px;color: rgba(0, 0, 0, 0.85);cursor: pointer;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;letter-spacing: normal;text-align: left;background-color: rgb(255, 255, 255);'>\u003Cspan style=\"cursor: pointer;font-size: 22px;color: rgb(0, 0, 0);line-height: 1.5em;letter-spacing: 0em;font-weight: bold;display: block;\">Conclusion: Open-ended Socratic learning is possible\u003C/span>\u003C/h2>\u003Cp style='margin-bottom: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;padding-top: 8px;padding-bottom: 8px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;background-color: rgb(255, 255, 255);'>We start from recursive self-improvement in closed systems and explore its potential on the path toward artificial general intelligence (AGI). At this stage, we can optimistically conclude:\u003C/p>\u003Cul style='margin-top: 8px;margin-bottom: 8px;cursor: pointer;padding-left: 25px;color: rgb(0, 0, 0);font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-size: 16px;letter-spacing: normal;text-align: left;background-color: rgb(255, 255, 255);' class=\"list-paddingleft-1\">\u003Cli style=\"cursor: pointer;\">\u003Csection style=\"cursor: pointer;margin-top: 5px;margin-bottom: 5px;color: rgb(1, 1, 1);line-height: 1.8em;letter-spacing: 0em;\">, and its main challenges (feedback and coverage) are already widely known.\u003C/section>\u003C/li>\u003Cli style=\"cursor: pointer;\">\u003Csection style=\"cursor: pointer;margin-top: 5px;margin-bottom: 5px;color: rgb(1, 1, 1);line-height: 1.8em;letter-spacing: 0em;\">provides a constructive starting point for addressing these challenges and also offers direction on how to formulate practical research agendas.\u003C/section>\u003C/li>\u003C/ul>\u003Cp style='margin-bottom: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;padding-top: 8px;padding-bottom: 8px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;background-color: rgb(255, 255, 255);'>Although specific roadmaps await further refinement in the future, the overall research direction has already become clear. It is particularly noteworthy that the diversity and richness of language games remain an underestimated important dimension. We believe that an ideal starting point is exploring processes that can generate open-ended games.\u003C/p>\u003Cp style='margin-bottom: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;padding-top: 8px;padding-bottom: 8px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;background-color: rgb(255, 255, 255);'>Interestingly, we submit these ideas for testing in the academic domain rather than engaging in self-talk within a closed system.\u003C/p>\u003Ch2 style='margin-top: 30px;margin-bottom: 15px;color: rgba(0, 0, 0, 0.85);cursor: pointer;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;letter-spacing: normal;text-align: left;background-color: rgb(255, 255, 255);'>\u003Cspan style=\"cursor: pointer;font-size: 22px;color: rgb(0, 0, 0);line-height: 1.5em;letter-spacing: 0em;font-weight: bold;display: block;\">Netizen comments\u003C/span>\u003C/h2>\u003Cp style='margin-bottom: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;padding-top: 8px;padding-bottom: 8px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;background-color: rgb(255, 255, 255);'>\"DeepMind's Socratic learning is a huge leap towards AGI! The concept of achieving self-improvement through language games and autonomous learning is exciting, and I look forward to its future development.\"\u003C/p>\u003Cp style='margin-bottom: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;padding-top: 8px;padding-bottom: 8px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;background-color: rgb(255, 255, 255);'>\"Will AI teach humans in our dreams?\"\u003C/p>\u003Cp style='margin-bottom: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;padding-top: 8px;padding-bottom: 8px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;background-color: rgb(255, 255, 255);'>\"At least Google has brought us this gift! OpenAI is starting to get boring.\"\u003C/p>\u003Cp style='margin-bottom: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;padding-top: 8px;padding-bottom: 8px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;background-color: rgb(255, 255, 255);'>\"This method of self-learning in a closed environment is fascinating. Through self-play and structured interactions, the agent can generate its own training data and enhance capabilities without continuous human intervention.\"\u003C/p>\u003Cp style='margin-bottom: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;padding-top: 8px;padding-bottom: 8px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;background-color: rgb(255, 255, 255);'>\"These systems must learn to ask the right questions, not just solve the problems we feed them.\"\u003C/p>\u003Cp style='margin-bottom: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;padding-top: 8px;padding-bottom: 8px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;background-color: rgb(255, 255, 255);'>\"A self-modifying system might break the constraints of existing architectures, but does this also mean AI could make unpredictable changes? How do we safely test and deploy such systems?\"\u003C/p>\u003Cp style='margin-bottom: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;padding-top: 8px;padding-bottom: 8px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;background-color: rgb(255, 255, 255);'>\"Language games are powerful iterative learning sandboxes. They allow AI to generate its own questions, solve them, and optimize reasoning abilities, marking a major leap towards AGI.\"\u003C/p>\u003Cp style='margin-bottom: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;padding-top: 8px;padding-bottom: 8px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;background-color: rgb(255, 255, 255);'>\"Sounds absolutely amazing.\"\u003C/p>\u003Cp style='margin-bottom: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;padding-top: 8px;padding-bottom: 8px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;background-color: rgb(255, 255, 255);'>\u003Cbr>\u003C/p>\u003Cp style=\"display: none;\">\u003Cmp-style-type data-value=\"3\">\u003C/mp-style-type>\u003C/p>\u003C/div>",[257,265,273,282,289,297,305,312],{"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},535,"89b0ede708d82451aba55968e3256afa","2022-05-21","cfab1ba8c67c7c838db98d666f02a132","8a672e2640dbb1f0c5104e1bcca53b66","article_res/cover/58976c3d3e60726c98d72af673ba7a2a.jpeg","article_res/cover/9b01b859fd5a9b8804bc7b742b8b8e38.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},560,"b7532d4680b9040409da07dd28c07415","2022-04-26","8b3607d0f4181a3cb6ffdccf7185f09b","b6ab1def25571ad28c3e9f8a8b29785c","article_res/cover/8a6cb0556055cb02137b8862f5fad8af.jpeg","article_res/cover/f20437d9f5bc73b8bedef7a244b6c363.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},289,"f230904c80595b43dde20905038bdf0b","2024-04-29","bc27fa490c4d0d525bac812fc0793534","#AI Index Report 2024","909ae1d433b7143c5c91742faa392212","article_res/cover/4bb85904c745f70a0b2f4e7cc85acae0.jpeg","article_res/cover/454cabc58508336adba0dad628d72c8d.jpeg",{"id":283,"title_md5":284,"publish_date":285,"author_md5":269,"is_original":4,"collection":5,"summary_md5":286,"cover_url":287,"cover_url_1_1":288},567,"2c4ece4a9a7dcc20b75e086a52cd3ed1","2022-04-19","91c2b7aa99eccbd47dbae562097bf515","article_res/cover/8c0e9a3a98bbcb1e7dc7bd1d82581ad2.jpeg","article_res/cover/f30fe693548f114d1b73677f1511e5e8.jpeg",{"id":290,"title_md5":291,"publish_date":292,"author_md5":277,"is_original":23,"collection":293,"summary_md5":294,"cover_url":295,"cover_url_1_1":296},48,"ceddd3a0051768a5d39d487af4ef8b7d","2025-02-05","#AI Agent #AI Avatar #Alibaba #AI Video Generator","29ca4273479e890a427f18a993572631","article_res/cover/b4b09d99258fcd5045abbea17c3ad843.jpeg","article_res/cover/7d789b6343a2bdb2c8748571d9440ef1.jpeg",{"id":298,"title_md5":299,"publish_date":300,"author_md5":277,"is_original":23,"collection":301,"summary_md5":302,"cover_url":303,"cover_url_1_1":304},25,"78ee9c62989c96701d84c5911874010c","2025-02-27","#Alibaba #AI Video Editor #Animate Anyone #AI Video Generation","9cfbe107bd45f72e2967e6a339e18f8f","article_res/cover/450c28bb351bd7dd1aa9ab908b494204.jpeg","article_res/cover/6bfa652b1433ba6033e45ac848042745.jpeg",{"id":306,"title_md5":307,"publish_date":308,"author_md5":277,"is_original":23,"collection":73,"summary_md5":309,"cover_url":310,"cover_url_1_1":311},162,"a581849d2e18d3608ea435dbc8912dfa","2024-10-02","f6a80b5653ce78c9dc376d65166ceaff","article_res/cover/509ff9fe9bfe754efd81da81384a9c1c.jpeg","article_res/cover/0dc5c996a691517e119efdd32f463512.jpeg",{"id":313,"title_md5":314,"publish_date":315,"author_md5":316,"is_original":4,"collection":5,"summary_md5":317,"cover_url":318,"cover_url_1_1":319},605,"c55b13d7599231f8d70474738ed3695b","2022-03-12","311a46cfdaa3afda544e9285644f70d7","1baf4d49403c8b373c0694f571283ea9","article_res/cover/b8b7c3b1377d8123bafbb8fd171f029c.jpeg","article_res/cover/2994594e2c4eef4b4732bfe9d526ffe0.jpeg",{"related":321,"small":361},[322,331,338,346,353],{"id":323,"publish_date":324,"is_original":4,"collection":325,"cover_url":326,"cover_url_1_1":327,"title":328,"summary":329,"author":330},501,"2022-11-13","#History","article_res/cover/af2aaf3b251d850acc3ed68e521fd9f0.jpeg","article_res/cover/daa0e5b18cc801b8a37260dce1e082c3.jpeg","Notes on \"Ming History: Overview and Special Topics\"","I opened the history book and found that it had no dates, with each crooked page written with the words 'benevolence, righteousness, morality'. I couldn't sleep, so I carefully read through the night, and only then did I see the real words in between the lines - the whole book was filled with two words: 'cannibalism'! - Lu Xun","Study Notes",{"id":332,"publish_date":333,"is_original":23,"collection":5,"cover_url":334,"cover_url_1_1":335,"title":336,"summary":337,"author":28},314,"2024-03-16","article_res/cover/f9d727dc25194bfbd719668aabaca6f5.jpeg","article_res/cover/884e54049dfda0ef62cd4beb83757df2.jpeg","Fabric Open Source Project - Crowdsourced AI Prompt Sets for Specific Problems","fabric is an open-source framework for augmenting humans using AI and solving problems using a crowdsourced prompt.",{"id":339,"publish_date":340,"is_original":23,"collection":341,"cover_url":342,"cover_url_1_1":343,"title":344,"summary":345,"author":28},45,"2025-02-08","#AI Agent #Object Detection","article_res/cover/b6dc16af6eeb0bf4f16993e2d31500c7.jpeg","article_res/cover/52519e83aef4083ebdf78cc303244a7a.jpeg","Andrew Ng's latest release on Agent target detection: Agentic Object Detection","Reasoning-driven object detection: human-like precision via text prompts without the overhead of custom training",{"id":347,"publish_date":348,"is_original":23,"collection":325,"cover_url":349,"cover_url_1_1":350,"title":351,"summary":352,"author":28},192,"2024-08-19","article_res/cover/4df67464bfe1776bc5eff7f08b21354f.jpeg","article_res/cover/507a86384d673bba5fd0a00d305d2421.jpeg","The Palace of Wisdom and the Spirit of Openness","The House of Wisdom: How Arabic Science Saved Ancient Knowledge and Gave Us the Renaissance.",{"id":354,"publish_date":355,"is_original":23,"collection":356,"cover_url":357,"cover_url_1_1":358,"title":359,"summary":360,"author":28},307,"2024-03-28","#LLM","article_res/cover/27c22d877aa4d827c74730a40932ef64.jpeg","article_res/cover/b925b57062ba315da3067ee08426cf12.jpeg","Large Language Models: A Survey","Scaling - a power law between compute, training data size and number of model parameters.",[362,368,374],{"title":10,"list":363},[364,365,366,367],{"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":369},[370,371,372,373],{"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},{"id":235,"publish_date":236,"is_original":23,"collection":73,"cover_url":237,"cover_url_1_1":238,"title":239,"summary":240,"author":28},{"title":242,"list":375},[],[8,9,10],[8,12,13,14,9,10,15,16,17,18],["Reactive",245],1754646409447]