In this blog, we will summarize the latex code of most fundamental equations of transfer learning(TL). Different from multi-task learning, transfer learning models aims to achieve the best performance on target domain (minimized target domain test errors), not the performance of source domain. Typical transfer learning methods including domain adaptation(DA), feature sub-space alignment, etc. In this post, we will dicuss more details of TL equations, including many sub-areas like domain adaptation, H-divergence, Domain-Adversarial Neural Networks(DANN), which are useful as quick reference for your research.

In this blog, we will give a brief introduction of what is

AI agent research have attracted a lot of attention with the rapid development of Large Language Model (LLM) and Reasoning (RL or CoT Chain of Thought) technology. Generative AI Agents use LLM to plan, act and reflect on the memories from the environment as well as the interactions from other agents, just like the environment in Stanford's Generative Agents paper "Generative Agents: Interactive Simulacra of Human Behavior". The simulation environment usually consists of a environment (Such as A Small Town) and multiple AI Agents taking actions to make plans and achieve goals. Before making plans and taking actions, agents will retrieve related information from memories, which is a list of descriptions of the timestamp and exact events, such as "2023-02-03 Someone is watching a movie...". During multi-agents simulation, each agent will have its own timeline and their actions are usually asynchronous. In this blog, we will give a review of the tools of AI Agents Visualization, especially in the situation where there are multiple agents with asynchronous actions during the simulation.

This blog summarize the latest research development of summarization papers published in ACL2023 conferences. This year there are total 49 papers related to summarization in ACL2023. Most of the authors' affiliations are top research institutes (Google Research, DeepMind, Meta FAIR) and universities (Stanford, Berkeley, MIT, CMU and others).

This blog summarizes the latest research development of dialogue and large language models (LLM) papers published in ACL2023 conferences. This year there are total 79 papers related to dialogue in ACL2023. Most of the authors' affiliations are top research institutes (Google Research, DeepMind, Meta FAIR) and universities (Stanford, Berkeley, MIT, CMU and others).

This blog summarizes the latest research development of speech papers published in ACL2023 conferences. This year there are total 54 papers related to speech in ACL2023. Most of the authors' affiliations are top research institutes (Google Research, DeepMind, Meta FAIR) and universities (Stanford, Berkeley, MIT, CMU and others).

This blog summarizes the latest research development of retrieval papers published in ACL2023 conferences. This year there are total 76 papers related to retrieval in ACL2023. Most of the authors' affiliations are top research institutes (Google Research, DeepMind, Meta FAIR) and universities (Stanford, Berkeley, MIT, CMU and others).

大家在使用抖音(Douyin Tiktok)的AI搜索功能的时候，遇到了哪些好的体验和有问题的体验？请麻烦写一下当时输入的条件，比如prompt输入文本，或者是上传截图。

大家在使用快手(Kuaishou)的AI搜索功能的时候，遇到了哪些好的体验和有问题的体验？请麻烦写一下当时输入的条件，比如prompt输入文本，或者是上传截图。

大家在使用知乎直答(Zhihu)AI搜索功能的时候，遇到了哪些好的体验和有问题的体验？请麻烦写一下当时输入的条件，比如prompt输入文本，或者是上传截图。

大家在使用知乎直答(Zhihu)AI搜索功能的时候，遇到了哪些好的体验和有问题的体验？请麻烦写一下当时输入的条件，比如prompt输入文本，或者是上传截图。

大家在使用拼多多(PPD Temu)APP的搜索推荐Search and Recommendation 功能的时候遇到了哪些好的体验和有问题的体验？请麻烦写明复现条件，比如prompt输入文本，上传截图。

大家在使用支付宝(Alipay)APP的搜索推荐Search and Recommendation 功能的时候遇到了哪些好的体验和有问题的体验？请麻烦写明复现条件，比如prompt输入文本，上传截图。

大家在使用淘宝(Taobao)APP的搜索推荐Search and Recommendation 功能的时候遇到了哪些好的体验和有问题的体验？请麻烦写明复现条件，比如prompt输入文本，上传截图。

大家在使用京东(JD)APP的搜索推荐Search and Recommendation 功能的时候遇到了哪些好的体验和有问题的体验？请麻烦写明复现条件，比如prompt输入文本，上传截图。

大家在使用知乎(Zhihu)APP的搜索推荐Search and Recommendation 功能的时候遇到了哪些好的体验和有问题的体验？请麻烦写明复现条件，比如prompt输入文本，上传截图。

大家在使用微信(WeChat)APP的AI问答功能的时候，遇到了哪些好的体验和有问题的体验？请麻烦写明复现条件，比如prompt输入文本，上传截图。

大家在使用微信(WeChat)APP的搜索推荐Search and Recommendation 功能的时候遇到了哪些好的体验和有问题的体验？请麻烦写明复现条件，比如prompt输入文本，上传截图。

大家在使用小红书(Xiaohongshu)APP的搜索推荐Search and Recommendation 功能的时候遇到了哪些好的体验和有问题的体验？请麻烦写明复现条件，比如prompt输入文本，上传截图。

大家在使用快手(Kuaishou Kwai)短视频的搜索推荐Search and Recommendation 功能的时候遇到了哪些好的体验和有问题的体验？请麻烦写明复现条件，比如prompt输入文本，上传截图。

We are witnessing great success in recent development of generative Artificial Intelligence in many fields, such as AI assistant, Chatbot, AI Writer. Among all the AI native products, AI Search Engine such as Perplexity, Gemini and SearchGPT are most attrative to website owners, bloggers and web content publishers. AI Search Engine is a new tool to provide answers directly to users' questions (queries). In this blog, we will give some brief introduction to basic concepts of AI Search Engine, including Large Language Models (LLM), Retrieval-Augmented Generation(RAG), Citations and Sources. Then we will highlight some majors differences between traditional Search Engine Optimization (SEO) and Generative Engine Optimization(GEO). And then we will cover some latest research and strategies to help website owners or content publishers to better optimize their content in Generative AI Search Engines.

大家在使用抖音视频的搜索推荐Search and Recommendation 功能的时候遇到了哪些好的体验和有问题的体验？请麻烦写明复现条件，比如prompt输入文本，上传截图。

In this blog, we will give you a brief introduction of most recent progress in On-Device Recommendation (Edge Recommendation) in real-world applications. Mobile AI systems and applications have been more popular due to increasing number of mobile devices and technology developments in deep learning based methods, e.g. model compression, distillation and so on. In recent years, on-device recommendations have enpowered many Mobile Apps to better respond to users' most real-time behaviors on mobile deivces, including clicks, scroll-donwns, likes, and many others. We will introduce three applications, including EdgeRec in Taobao, searchbar background words reranking in Alipay, search result reranking in Meituan-Dianping, short-video recommendation in KuaiShou, TfLite Implementation of Tensorflow, etc.

In this blog, we will summarize the latex code of equations of Graph Neural Network(GNN) models, which are useful as quick reference for your research. For common notation, we denote G=(V,E) as the graph. V as the set of nodes with size |V|=N, and E as the set of N_e edges as |E| = N_e. A is denoted as the adjacency matrix. For each node v, we use h_v and o_v as hidde state and output vector of each node.

This blog summarizes the latest research development of Retrieval papers published in KDD2023 conferences. This year there are total 8 papers related to Retrieval in KDD2023. Most of the authors' affiliations are top research institutes (Google Research, DeepMind, Meta FAIR) and universities (Stanford, Berkeley, MIT, CMU and others).

In this blog, we are introducing how you can use financial chatbot and AI-assistant writer to help you facilitate daily financial work, such as getting realtime stock price quote from global markets (US: NYSE,NASDAQ; China: HKEX, Shanghai,Shenzhen, etc.), consulting investment advice, consensus ratings and target price of stocks from analysts of multiple organizations (Schwab.com, MarketBeats, Zacks.com) and compare them, finding the meaning of complicated financial terms such as CPI, Put Option, Sharpe Ratio. You can also use ChatGPT to act as personally financial assistant, track your portfolio's stock names and show realtime changes. For content generation, you can use the AI-Writer assistant to facilitate writing reports, which integrates many APIs and other ChatGPT style Large Language Model to automatically generate text contents such as market research, financial reports of listed companies, etc.

This blog summarizes the latest research development of GNN papers published in KDD2023 conferences. This year there are total 9 papers related to GNN in KDD2023. Most of the authors' affiliations are top research institutes (Google Research, DeepMind, Meta FAIR) and universities (Stanford, Berkeley, MIT, CMU and others).

This blog summarizes the latest research development of Recommendation papers published in KDD2023 conferences. This year there are total 44 papers related to Recommendation in KDD2023. Most of the authors' affiliations are top research institutes (Google Research, DeepMind, Meta FAIR) and universities (Stanford, Berkeley, MIT, CMU and others).

This blog summarizes the latest research development of Knowledge Graph papers published in KDD2023 conferences. This year there are total 11 papers related to Knowledge Graph in KDD2023. Most of the authors' affiliations are top research institutes (Google Research, DeepMind, Meta FAIR) and universities (Stanford, Berkeley, MIT, CMU and others).