[{"data":1,"prerenderedAt":374},["ShallowReactive",2],{"$fgukOamtKU1RtUiMFsqdObttmqPPQz0uc7bl_gj_LyX0":3,"$f0ebiWh4EohKJJ5sUkIiUllO-QCfCRRmX5K9AGDYuoh8":245,"article-439":373},{"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? Would turning them off be murder? 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":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":320,"category":371,"tag":372},439,"2023-06-25","#Alignment","_tMvhVVRWj-8NOFyupU6dQ","article_res/cover/86f79dea711b5b27ded77383a9cb4cb4.jpeg","article_res/cover/1259e63b22b4e2900611a64d959a052f.jpeg","Artificial Intelligence Glossary (AI glossary)","\"The beginning of wisdom is the definition of terms.\" - Socrates","\u003Cdiv class=\"rich_media_content js_underline_content\n                       autoTypeSetting24psection\n            \" id=\"js_content\">\u003Csection data-tool=\"mdnice编辑器\" data-website=\"https://www.mdnice.com\" style='font-size: 16px;color: black;padding: 0px 10px;line-height: 1.6;word-spacing: 0px;letter-spacing: 0px;word-break: break-word;overflow-wrap: break-word;text-align: left;font-family: Optima-Regular, Optima, PingFangSC-light, PingFangTC-light, \"PingFang SC\", Cambria, Cochin, Georgia, Times, \"Times New Roman\", serif;'>\u003Cp data-tool=\"mdnice编辑器\" style=\"font-size: 16px;padding-top: 8px;padding-bottom: 8px;margin: 0;line-height: 26px;color: black;\">There are always various terms in the AI field that are difficult to distinguish and understand. Today, I will make a summary, which will also help me with future lookups.\u003C/p>\u003Cp style=\"text-align: center;\">\u003Cimg class=\"rich_pages wxw-img js_insertlocalimg\" data-ratio=\"0.6733966745843231\" data-s=\"300,640\" data-type=\"png\" data-w=\"842\" style=\"\" src=\"./assets/17434960091070.043149050106279896.png\">\u003C/p>\u003Col data-tool=\"mdnice编辑器\" style=\"margin-top: 8px;margin-bottom: 8px;padding-left: 25px;color: black;list-style-type: decimal;\" class=\"list-paddingleft-1\">\u003Cli>\u003Csection style=\"margin-top: 5px;margin-bottom: 5px;line-height: 26px;text-align: left;color: rgb(1,1,1);font-weight: 500;\">\u003Cp style=\"font-size: 16px;padding-top: 8px;padding-bottom: 8px;margin: 0;line-height: 26px;color: black;\">: Describe the different types of artificial intelligence and their stages of development.\u003C/p>\u003Cp style=\"text-align: center;\">\u003Cimg class=\"rich_pages wxw-img\" data-galleryid=\"\" data-ratio=\"0.5146666666666667\" data-s=\"300,640\" data-type=\"jpeg\" data-w=\"750\" style=\"\" src=\"./assets/17434960093280.29777260620797197.jpeg\">\u003C/p>\u003Cp>\u003Cbr>\u003C/p>\u003C/section>\u003C/li>\u003C/ol>\u003Cul style=\"margin-top: 8px;margin-bottom: 8px;padding-left: 25px;color: black;list-style-type: disc;\" class=\"list-paddingleft-1\">\u003Cli>\u003Csection style=\"margin-top: 5px;margin-bottom: 5px;line-height: 26px;text-align: left;color: rgb(1,1,1);font-weight: 500;\">Artificial Intelligence (AI): \"Intelligence\" demonstrated by systems created by humans, including learning, understanding, reasoning, perception, language recognition, etc.\u003C/section>\u003C/li>\u003Cli>\u003Csection style=\"margin-top: 5px;margin-bottom: 5px;line-height: 26px;text-align: left;color: rgb(1,1,1);font-weight: 500;\">Artificial General Intelligence (AGI): Refers to the ability of a machine to possess all levels of human intelligence.\u003C/section>\u003C/li>\u003Cli>\u003Csection style=\"margin-top: 5px;margin-bottom: 5px;line-height: 26px;text-align: left;color: rgb(1,1,1);font-weight: 500;\">Artificial Super Intelligence (ASI): Machine intelligence that surpasses the smartest and most creative levels of humans.\u003C/section>\u003C/li>\u003Cli>\u003Csection style=\"margin-top: 5px;margin-bottom: 5px;line-height: 26px;text-align: left;color: rgb(1,1,1);font-weight: 500;\">Explainable AI (XAI): A subfield of AI focused on creating transparent models that provide clear and understandable explanations for their decisions.\u003C/section>\u003C/li>\u003Cli>\u003Csection style=\"margin-top: 5px;margin-bottom: 5px;line-height: 26px;text-align: left;color: rgb(1,1,1);font-weight: 500;\">Chatbot: A program capable of communicating with humans through text or voice.\u003C/section>\u003C/li>\u003Cli>\u003Csection style=\"margin-top: 5px;margin-bottom: 5px;line-height: 26px;color: rgb(1, 1, 1);\">Agents: Entities that act and interact within an environment, such as agents in reinforcement learning.\u003C/section>\u003C/li>\u003Cli>\u003Csection style=\"margin-top: 5px;margin-bottom: 5px;line-height: 26px;text-align: left;color: rgb(1,1,1);font-weight: 500;\">Expert Systems: Computer systems that simulate the knowledge and judgment abilities of a human expert.\u003C/section>\u003C/li>\u003Cli>\u003Csection style=\"margin-top: 5px;margin-bottom: 5px;line-height: 26px;text-align: left;color: rgb(1,1,1);font-weight: 500;\">Emergence/Emergent Behavior: A new behavior or property that arises in a complex system, which does not exist in the individual parts of the system.\u003C/section>\u003C/li>\u003Cli>\u003Csection style=\"margin-top: 5px;margin-bottom: 5px;line-height: 26px;text-align: left;color: rgb(1,1,1);font-weight: 500;\">Generative AI: A type of AI that can create new content, such as music, articles, images, etc.\u003C/section>\u003C/li>\u003C/ul>\u003Cli>\u003Csection style=\"margin-top: 5px;margin-bottom: 5px;line-height: 26px;text-align: left;color: rgb(1,1,1);font-weight: 500;\">\u003Cp style=\"font-size: 16px;padding-top: 8px;padding-bottom: 8px;margin: 0;line-height: 26px;color: black;\">: Involve various types of machine learning methods and techniques.\u003C/p>\u003Cp style=\"text-align: center;\">\u003Cimg class=\"rich_pages wxw-img\" data-galleryid=\"\" data-ratio=\"0.5083333333333333\" data-s=\"300,640\" data-type=\"png\" data-w=\"1080\" style=\"\" src=\"./assets/17434960091300.9280716723730618.png\">\u003C/p>\u003Cp>\u003Cbr>\u003C/p>\u003C/section>\u003C/li>\u003Cul style=\"margin-top: 8px;margin-bottom: 8px;padding-left: 25px;color: black;list-style-type: disc;\" class=\"list-paddingleft-1\">\u003Cli>\u003Csection style=\"margin-top: 5px;margin-bottom: 5px;line-height: 26px;text-align: left;color: rgb(1,1,1);font-weight: 500;\">Machine Learning (ML): A method for implementing artificial intelligence where machines improve their performance by learning from and understanding data.\u003C/section>\u003C/li>\u003Cli>\u003Csection style=\"margin-top: 5px;margin-bottom: 5px;line-height: 26px;text-align: left;color: rgb(1,1,1);font-weight: 500;\">Supervised Learning: A type of machine learning where the model is provided with labeled training data.\u003C/section>\u003C/li>\u003Cli>\u003Csection style=\"margin-top: 5px;margin-bottom: 5px;line-height: 26px;text-align: left;color: rgb(1,1,1);font-weight: 500;\">Unsupervised Learning: A type of machine learning where the model is not provided with labeled training data and must identify patterns in the data on its own.\u003C/section>\u003C/li>\u003Cli>\u003Csection style=\"margin-top: 5px;margin-bottom: 5px;line-height: 26px;text-align: left;color: rgb(1,1,1);font-weight: 500;\">Reinforcement Learning: A type of machine learning where an agent learns to make decisions by taking actions in an environment to maximize some reward.\u003C/section>\u003C/li>\u003Cli>\u003Csection style=\"margin-top: 5px;margin-bottom: 5px;line-height: 26px;text-align: left;color: rgb(1,1,1);font-weight: 500;\">Deep Learning: A technique for implementing machine learning that involves learning through the simulation of neural networks in the human brain.\u003C/section>\u003C/li>\u003Cli>\u003Csection style=\"margin-top: 5px;margin-bottom: 5px;line-height: 26px;text-align: left;color: rgb(1,1,1);font-weight: 500;\">Transfer Learning: A method in machine learning that uses a pre-trained model for a new problem.\u003C/section>\u003C/li>\u003Cli>\u003Csection style=\"margin-top: 5px;margin-bottom: 5px;line-height: 26px;text-align: left;color: rgb(1,1,1);font-weight: 500;\">Zero-shot Learning: A type of machine learning where the model makes predictions on conditions not seen during training, without any fine-tuning.\u003C/section>\u003C/li>\u003C/ul>\u003Cli>\u003Csection style=\"margin-top: 5px;margin-bottom: 5px;line-height: 26px;text-align: left;color: rgb(1,1,1);font-weight: 500;\">\u003Cp style=\"font-size: 16px;padding-top: 8px;padding-bottom: 8px;margin: 0;line-height: 26px;color: black;\">: Specific frameworks and models for building and training machine learning models.\u003C/p>\u003Cp style=\"text-align: center;\">\u003Cimg class=\"rich_pages wxw-img\" data-galleryid=\"\" data-ratio=\"0.5370370370370371\" data-s=\"300,640\" data-type=\"png\" data-w=\"1080\" style=\"\" src=\"./assets/17434960090130.5704707549344918.png\">\u003C/p>\u003Cp>\u003Cbr>\u003C/p>\u003C/section>\u003C/li>\u003Cul style=\"margin-top: 8px;margin-bottom: 8px;padding-left: 25px;color: black;list-style-type: disc;\" class=\"list-paddingleft-1\">\u003Cli>\u003Csection style=\"margin-top: 5px;margin-bottom: 5px;line-height: 26px;text-align: left;color: rgb(1,1,1);font-weight: 500;\">TensorFlow: An open-source machine learning platform developed by Google for building and training machine learning models.\u003C/section>\u003C/li>\u003Cli>\u003Csection style=\"margin-top: 5px;margin-bottom: 5px;line-height: 26px;text-align: left;color: rgb(1,1,1);font-weight: 500;\">Large Language Model (LLM): A model trained on a large amount of text data, used for understanding and generating human language.\u003C/section>\u003C/li>\u003Cli>\u003Csection style=\"margin-top: 5px;margin-bottom: 5px;line-height: 26px;color: rgb(1, 1, 1);\">Neural Network: A computational model that mimics the working mechanism of the human brain, used for data analysis and prediction.\u003C/section>\u003C/li>\u003Cli>\u003Csection style=\"margin-top: 5px;margin-bottom: 5px;line-height: 26px;color: rgb(1, 1, 1);\">Attention: A mechanism in neural network models used to assign different levels of importance when processing information.\u003C/section>\u003C/li>\u003Cli>\u003Csection style=\"margin-top: 5px;margin-bottom: 5px;line-height: 26px;text-align: left;color: rgb(1,1,1);font-weight: 500;\">Transformer: A specific type of neural network architecture mainly used for processing sequential data such as natural language.\u003C/section>\u003C/li>\u003Cli>\u003Cp>\u003Cspan style=\"\">Convolutional Neural Network (CNN): A form of deep learning widely applied in image processing. It automatically and effectively learns local image features through convolution operations.\u003C/span>\u003C/p>\u003C/li>\u003Cli>\u003Cp>\u003Cspan style=\"\">\u003Cspan style='color: rgb(0, 0, 0);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;'>Recurrent Neural Network (RNN): A type of network structure in machine learning particularly suitable for processing and predicting sequential data. The neurons in RNNs not only receive the influence of the current input but also retain historical information from previous inputs, forming a network structure with \"memory\" functionality.\u003C/span>\u003C/span>\u003C/p>\u003C/li>\u003Cli>\u003Csection style=\"margin-top: 5px;margin-bottom: 5px;line-height: 26px;color: rgb(1, 1, 1);\">Generative Adversarial Network (GAN): A deep learning model and one of the most promising approaches for unsupervised learning on complex distributions in recent years. It is a training method that allows two neural networks to learn by competing with each other.\u003C/section>\u003C/li>\u003Cli>\u003Csection style=\"margin-top: 5px;margin-bottom: 5px;line-height: 26px;text-align: left;color: rgb(1,1,1);font-weight: 500;\">GPT (Generative Pretrained Transformer): A pre-trained generative Transformer model developed by OpenAI, used for various natural language processing tasks.\u003C/section>\u003C/li>\u003Cli>\u003Csection style=\"margin-top: 5px;margin-bottom: 5px;line-height: 26px;text-align: left;color: rgb(1,1,1);font-weight: 500;\">CLIP (Contrastive Language-Image Pre-Training): A model developed by OpenAI that can understand natural language instructions and generate corresponding images.\u003C/section>\u003C/li>\u003C/ul>\u003Cli>\u003Csection style=\"margin-top: 5px;margin-bottom: 5px;line-height: 26px;text-align: left;color: rgb(1,1,1);font-weight: 500;\">\u003Cp style=\"font-size: 16px;padding-top: 8px;padding-bottom: 8px;margin: 0;line-height: 26px;color: black;\">: Techniques and methods describing how to train and optimize machine learning models.\u003C/p>\u003Cp style=\"text-align: center;\">\u003Cimg class=\"rich_pages wxw-img\" data-galleryid=\"\" data-ratio=\"0.8\" data-s=\"300,640\" data-type=\"png\" data-w=\"1080\" style=\"\" src=\"./assets/17434960095620.21369876087236261.png\">\u003C/p>\u003Cp>\u003Cbr>\u003C/p>\u003C/section>\u003C/li>\u003Cul style=\"margin-top: 8px;margin-bottom: 8px;padding-left: 25px;color: black;list-style-type: disc;\" class=\"list-paddingleft-1\">\u003Cli>\u003Csection style=\"margin-top: 5px;margin-bottom: 5px;line-height: 26px;text-align: left;color: rgb(1,1,1);font-weight: 500;\">Training Data: The dataset used to train a machine learning model.\u003C/section>\u003C/li>\u003Cli>\u003Csection style=\"margin-top: 5px;margin-bottom: 5px;line-height: 26px;color: rgb(1, 1, 1);\">Test Data: Data used for detecting model construction, which is only utilized during model validation to evaluate the accuracy of the model. It must not be used in the model construction process, as this would lead to overfitting.\u003C/section>\u003C/li>\u003Cli>\u003Csection style=\"margin-top: 5px;margin-bottom: 5px;line-height: 26px;text-align: left;color: rgb(1,1,1);font-weight: 500;\">Validation Data: A subset of the dataset in machine learning used to adjust hyperparameters of the model, separate from the training and test datasets.\u003C/section>\u003C/li>\u003Cli>\u003Csection style=\"margin-top: 5px;margin-bottom: 5px;line-height: 26px;text-align: left;color: rgb(1,1,1);font-weight: 500;\">Gradient Descent: An optimization algorithm used to find the minimum value of a function, commonly applied in the training process of machine learning and deep learning models.\u003C/section>\u003C/li>\u003Cli>\u003Csection style=\"margin-top: 5px;margin-bottom: 5px;line-height: 26px;text-align: left;color: rgb(1,1,1);font-weight: 500;\">Back Propagation: A method for calculating gradients in neural networks, often used in the training of neural networks.\u003C/section>\u003C/li>\u003Cli>\u003Csection style=\"margin-top: 5px;margin-bottom: 5px;line-height: 26px;text-align: left;color: rgb(1,1,1);font-weight: 500;\">Fine-tuning: A process of adjusting a pre-trained model to adapt to a new task.\u003C/section>\u003C/li>\u003Cli>\u003Csection style=\"margin-top: 5px;margin-bottom: 5px;line-height: 26px;color: rgb(1, 1, 1);\">Alignment: The degree to which an AI's behavior is consistent with its designer's intentions.\u003C/section>\u003C/li>\u003Cli>\u003Csection style=\"margin-top: 5px;margin-bottom: 5px;line-height: 26px;color: rgb(1, 1, 1);\">Prompt: Input information used to trigger specific output generation from an AI model.\u003C/section>\u003C/li>\u003Cli>\u003Csection style=\"margin-top: 5px;margin-bottom: 5px;line-height: 26px;color: rgb(1, 1, 1);\">Chain of Thought (CoT): Refers to the thinking process of an AI model when processing information and making decisions.\u003C/section>\u003C/li>\u003C/ul>\u003Cli>\u003Csection style=\"margin-top: 5px;margin-bottom: 5px;line-height: 26px;text-align: left;color: rgb(1,1,1);font-weight: 500;\">\u003Cp style=\"font-size: 16px;padding-top: 8px;padding-bottom: 8px;margin: 0;line-height: 26px;color: black;\">：Describe how to evaluate the performance of a machine learning model and the possible issues that may arise.\u003C/p>\u003Cp style=\"text-align: center;\">\u003Cimg class=\"rich_pages wxw-img\" data-galleryid=\"\" data-ratio=\"0.7922636103151862\" data-s=\"300,640\" data-type=\"png\" data-w=\"698\" style=\"\" src=\"./assets/17434960098180.5959530849857824.png\">\u003C/p>\u003Cp>\u003Cbr>\u003C/p>\u003C/section>\u003C/li>\u003Cul style=\"margin-top: 8px;margin-bottom: 8px;padding-left: 25px;color: black;list-style-type: disc;\" class=\"list-paddingleft-1\">\u003Cli>\u003Csection style=\"margin-top: 5px;margin-bottom: 5px;line-height: 26px;text-align: left;color: rgb(1,1,1);font-weight: 500;\">Overfitting: When a statistical model or machine learning algorithm matches the training data too closely, resulting in decreased performance on new data.\u003C/section>\u003C/li>\u003Cli>\u003Csection style=\"margin-top: 5px;margin-bottom: 5px;line-height: 26px;text-align: left;color: rgb(1,1,1);font-weight: 500;\">Underfitting: A modeling error that occurs when a statistical model or machine learning algorithm cannot adequately capture the underlying structure of the data.\u003C/section>\u003C/li>\u003Cli>\u003Csection style=\"margin-top: 5px;margin-bottom: 5px;line-height: 26px;text-align: left;color: rgb(1,1,1);font-weight: 500;\">Data Augmentation: A method to reduce model overfitting by transforming and expanding the training data to enhance the model's generalization ability.\u003C/section>\u003C/li>\u003Cli>\u003Csection style=\"margin-top: 5px;margin-bottom: 5px;line-height: 26px;color: rgb(1, 1, 1);\">Regularization: In machine learning, regularization is a technique used to prevent overfitting by adding a penalty term to the model's loss function.\u003C/section>\u003C/li>\u003C/ul>\u003Cli>\u003Csection style=\"margin-top: 5px;margin-bottom: 5px;line-height: 26px;text-align: left;color: rgb(1,1,1);font-weight: 500;\">\u003Cp style=\"font-size: 16px;padding-top: 8px;padding-bottom: 8px;margin: 0;line-height: 26px;color: black;\">: Describes the components of a machine learning model and how to adjust these components to improve model performance.\u003C/p>\u003C/section>\u003C/li>\u003Cul style=\"margin-top: 8px;margin-bottom: 8px;padding-left: 25px;color: black;list-style-type: disc;\" class=\"list-paddingleft-1\">\u003Cli>\u003Csection style=\"margin-top: 5px;margin-bottom: 5px;line-height: 26px;text-align: left;color: rgb(1,1,1);font-weight: 500;\">Parameters: Variables learned through data during the model training process, such as the weights in a neural network.\u003C/section>\u003C/li>\u003Cli>\u003Csection style=\"margin-top: 5px;margin-bottom: 5px;line-height: 26px;text-align: left;color: rgb(1,1,1);font-weight: 500;\">Hyperparameter Tuning: The process of finding the optimal hyperparameters to maximize model performance.\u003C/section>\u003C/li>\u003Cli>\u003Csection style=\"margin-top: 5px;margin-bottom: 5px;line-height: 26px;text-align: left;color: rgb(1,1,1);font-weight: 500;\">Hidden Layer: A layer between the input and output layers in a neural network, used to extract various features from the input.\u003C/section>\u003C/li>\u003C/ul>\u003Cli>\u003Csection style=\"margin-top: 5px;margin-bottom: 5px;line-height: 26px;text-align: left;color: rgb(1,1,1);font-weight: 500;\">\u003Cp style=\"font-size: 16px;padding-top: 8px;padding-bottom: 8px;margin: 0;line-height: 26px;color: black;\">: A hardware device designed to accelerate compute-intensive tasks, such as machine learning.\u003C/p>\u003C/section>\u003C/li>\u003Cul style=\"margin-top: 8px;margin-bottom: 8px;padding-left: 25px;color: black;list-style-type: disc;\" class=\"list-paddingleft-1\">\u003Cli>\u003Csection style=\"margin-top: 5px;margin-bottom: 5px;line-height: 26px;text-align: left;color: rgb(1,1,1);font-weight: 500;\">GPU (Graphics Processing Unit): A hardware device used to accelerate graphics and image processing tasks, also commonly used for deep learning computations.\u003C/section>\u003C/li>\u003Cli>\u003Csection style=\"margin-top: 5px;margin-bottom: 5px;line-height: 26px;text-align: left;color: rgb(1,1,1);font-weight: 500;\">TPU (Tensor Processing Unit): A type of microprocessor specifically developed by Google to accelerate machine learning workloads.\u003C/section>\u003C/li>\u003Cli>\u003Csection style=\"margin-top: 5px;margin-bottom: 5px;line-height: 26px;text-align: left;color: rgb(1,1,1);font-weight: 500;\">Accelerator: A microprocessor specifically designed to accelerate AI applications, which can significantly improve processing speed and efficiency.\u003C/section>\u003C/li>\u003Cli>\u003Csection style=\"margin-top: 5px;margin-bottom: 5px;line-height: 26px;text-align: left;color: rgb(1,1,1);font-weight: 500;\">\u003Cspan style='color: rgb(1, 1, 1);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;'>Compute\u003C/span>\u003Cspan style='color: rgb(1, 1, 1);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;'>(计算)：\u003C/span>\u003Cspan style='color: rgb(1, 1, 1);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;'>The computing resources used to perform AI tasks.\u003C/span>\u003Cbr>\u003C/section>\u003C/li>\u003C/ul>\u003Cli>\u003Csection style=\"margin-top: 5px;margin-bottom: 5px;line-height: 26px;text-align: left;color: rgb(1,1,1);font-weight: 500;\">\u003Cp style=\"font-size: 16px;padding-top: 8px;padding-bottom: 8px;margin: 0;line-height: 26px;color: black;\">\u003Cstrong style=\"font-weight: bold;color: black;\">Stable Diffusion Related\u003C/strong>\u003C/p>\u003Cp style=\"text-align: center;\">\u003Cstrong style=\"font-weight: bold;color: black;\">\u003Cimg class=\"rich_pages wxw-img\" data-galleryid=\"\" data-ratio=\"0.7962962962962963\" data-s=\"300,640\" data-type=\"webp\" data-w=\"1080\" style=\"\" src=\"./assets/17434960095520.49560353190954953.jpeg\">\u003C/strong>\u003C/p>\u003C/section>\u003C/li>\u003Cul style=\"margin-top: 8px;margin-bottom: 8px;padding-left: 25px;color: black;list-style-type: disc;\" class=\"list-paddingleft-1\">\u003Cli>\u003Csection style=\"margin-top: 5px;margin-bottom: 5px;line-height: 26px;text-align: left;color: rgb(1,1,1);font-weight: 500;\">Stable Diffusion: An image generation model published by CompVis in 2022, consisting of U-Net, VAE, and a text encoder, used to create specific types of images.\u003C/section>\u003C/li>\u003Cli>\u003Csection style=\"margin-top: 5px;margin-bottom: 5px;line-height: 26px;text-align: left;color: rgb(1,1,1);font-weight: 500;\">Model (Checkpoint): Also known as checkpoint files, these are pre-trained stable diffusion weights designed to create general or specific types of images.\u003C/section>\u003C/li>\u003Cli>\u003Csection style=\"margin-top: 5px;margin-bottom: 5px;line-height: 26px;text-align: left;color: rgb(1,1,1);font-weight: 500;\">(Low-Rank Adaptation, LoRA): LoRAs are smaller files (usually 10-200MB) that are used in conjunction with existing stable diffusion checkpoint models to introduce new themes into images, which can range from characters to art styles to poses.\u003C/section>\u003C/li>\u003Cli>\u003Csection style=\"margin-top: 5px;margin-bottom: 5px;line-height: 26px;text-align: left;color: rgb(1,1,1);font-weight: 500;\">VAE Aesthetic Embedding: A method that improves and repairs details, saturation, and other aspects of images.\u003C/section>\u003C/li>\u003Cli>\u003Csection style=\"margin-top: 5px;margin-bottom: 5px;line-height: 26px;text-align: left;color: rgb(1,1,1);font-weight: 500;\">Hypernetwork: Also known as a style model, this is a fine-tuning technique that attaches a small neural network to modify styles.\u003C/section>\u003C/li>\u003Cli>\u003Csection style=\"margin-top: 5px;margin-bottom: 5px;line-height: 26px;text-align: left;color: rgb(1,1,1);font-weight: 500;\">): A deep learning model published by Google Research and Boston University in 2022, used to fine-tune existing image generation models.\u003C/section>\u003C/li>\u003Cli>\u003Csection style=\"margin-top: 5px;margin-bottom: 5px;line-height: 26px;text-align: left;color: rgb(1,1,1);font-weight: 500;\">Text inversion: A type of embedding training method used to extract and learn textual information.\u003C/section>\u003C/li>\u003Cli>\u003Csection style=\"margin-top: 5px;margin-bottom: 5px;line-height: 26px;text-align: left;color: rgb(1,1,1);font-weight: 500;\">Safe tensors (safetensors): A model format developed by Huggingface for securely saving tensor data.\u003C/section>\u003C/li>\u003C/ul>\u003C/section>\u003Cp style=\"display: none;\">\u003Cmp-style-type data-value=\"3\">\u003C/mp-style-type>\u003C/p>\u003C/div>",[257,266,274,281,289,297,304,312],{"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},283,"21473d5e51d71b3f083e72fc954d9985","2024-05-05","bc27fa490c4d0d525bac812fc0793534","#AI Index Report 2024 #LLM 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Video Generator #Meta #AI Video Editor","07b54e7f8d863dd4a63548b9be016d5f","article_res/cover/9ef71241f1bd883f1b09e353139e324b.jpeg","article_res/cover/32e019ffdaf19f1a17ade55ec1caaf2f.jpeg",{"id":298,"title_md5":299,"publish_date":300,"author_md5":261,"is_original":4,"collection":5,"summary_md5":301,"cover_url":302,"cover_url_1_1":303},440,"2625737f4c4b641898eb12f03ddd434f","2023-06-24","3514c1dce56b91ffba23126867b67fef","article_res/cover/e5ac56e1640c601a518a9fb24370ec3e.jpeg","article_res/cover/659be0c95bfea0624423348bdd82434c.jpeg",{"id":305,"title_md5":306,"publish_date":307,"author_md5":261,"is_original":23,"collection":308,"summary_md5":309,"cover_url":310,"cover_url_1_1":311},11,"4fbd09f74c8435e264fd7d026a9bb163","2025-03-10","#AI Agents #AI Coder #Operator #OpenAI #Replit","892c7a882780396423fd6b01d8713bdd","article_res/cover/7158ed3b6308013fdcd6191afd7b33ee.jpeg","article_res/cover/7a04464dca913878218970fa718957a5.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},66,"e1cbbd2cfb7427ee271f64c58b568d32","2025-01-19","#Google #Imagen3 #AI Image Generator","ad19ef13bbd6671ff9f5d7bfd175416e","article_res/cover/56ae40decdd38deb3bc1b59a2efa02ed.jpeg","article_res/cover/8df243692026cd39a5b6ecbe2c6ec2ec.jpeg",{"related":321,"small":356},[322,330,339,347,348],{"id":323,"publish_date":324,"is_original":23,"collection":325,"cover_url":326,"cover_url_1_1":327,"title":328,"summary":329,"author":28},216,"2024-07-18","#Google #AI Animation","article_res/cover/109166e45c5d3e4518bce69e5fe628c6.jpeg","article_res/cover/843d204c751e0333a32b50840a4b0202.jpeg","Google's Still-Moving: Generating personalized video content through a few static reference images","A Research Exploration into Customized Video Generation without Customized Video Data",{"id":331,"publish_date":332,"is_original":4,"collection":333,"cover_url":334,"cover_url_1_1":335,"title":336,"summary":337,"author":338},421,"2023-08-11","#AI Agents #AI Agent","article_res/cover/0f85fa914e4afdf2a97cf7d8c3a40626.jpeg","article_res/cover/5d4575643ec458a8374558ce5d954db2.jpeg","Stanford AI Agents open-source virtual town with residents～","It contains our core simulation module for generative agents and their game environment.","雷内",{"id":340,"publish_date":341,"is_original":23,"collection":342,"cover_url":343,"cover_url_1_1":344,"title":345,"summary":346,"author":28},72,"2025-01-13","#AI 3D Generator","article_res/cover/f728004944569a58319534725d131f9c.jpeg","article_res/cover/8bb880a3de057f8e3d6dd68edad65421.jpeg","Hyper3D - 3D Asset Generation Tool","Controllable Large-scale Generative Model for Creating High-quality 3D Assets",{"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":349,"publish_date":350,"is_original":4,"collection":5,"cover_url":351,"cover_url_1_1":352,"title":353,"summary":354,"author":355},604,"2022-03-13","article_res/cover/b1e7e12d55932a445e91d45f896c486a.jpeg","article_res/cover/0c07557158250cca3a12a1ea1b9d9a48.jpeg","How to read financial statements - CF Statement of Cash Flows","Why focus on cash flows? Because a share of stock is a","Course notes",[357,363,369],{"title":10,"list":358},[359,360,361,362],{"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":364},[365,366,367,368],{"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":370},[],[8,9,10],[8,12,13,14,9,10,15,16,17,18],["Reactive",245],1754646416411]