Dialogue Agent Multimodal Visualization Tools for AI Systems: A Review. With the rapid development of generative AI technology, developing Chatbot Assistant, Dialogue and Agents have become frequent tasks for designer, developer, product manager, machine learning and AI practitioner. There are a lot data (usually in json format) exchange during different phases of prototyping, designing and developing modern AI systems, such as Chatbot Assistant, Agent, Dialogue, AI Image Generator, text to image, AI video generator, etc. There are potential needs to help visualize the data (json format or others) for AI system. In this blog, we will introduce you an online dialogue data visualization tool DeepNLP Dialogue Visualization to help visualize the agents and dialogue history with simply a json string data. And we will use the OpenAI Dialogue GPT model for a multi-turn dialogue developing for example.

Dialogue Agent Multimodal Visualization Tools for AI Systems: A Review. With the rapid development of generative AI technology, developing Chatbot Assistant, Dialogue and Agents have become frequent tasks for designer, developer, product manager, machine learning and AI practitioner. There are a lot data (usually in json format) exchange during different phases of prototyping, designing and developing modern AI systems, such as Chatbot Assistant, Agent, Dialogue, AI Image Generator, text to image, AI video generator, etc. There are potential needs to help visualize the data (json format or others) for AI system. In this blog, we will introduce you an online dialogue data visualization tool DeepNLP Dialogue Visualization to help visualize the agents and dialogue history with simply a json string data. And we will use the OpenAI Dialogue GPT model for a multi-turn dialogue developing for example.

Dialogue Agent Multimodal Visualization Tools for AI Systems: A Review. With the rapid development of generative AI technology, developing Chatbot Assistant, Dialogue and Agents have become frequent tasks for designer, developer, product manager, machine learning and AI practitioner. There are a lot data (usually in json format) exchange during different phases of prototyping, designing and developing modern AI systems, such as Chatbot Assistant, Agent, Dialogue, AI Image Generator, text to image, AI video generator, etc. There are potential needs to help visualize the data (json format or others) for AI system. In this blog, we will introduce you an online dialogue data visualization tool DeepNLP Dialogue Visualization to help visualize the agents and dialogue history with simply a json string data. And we will use the OpenAI Dialogue GPT model for a multi-turn dialogue developing for example.

In this blog, we will introduce the DeepNLP Equation workspace, which helps users better manage, edit, share and display their equations. In the platform, users can manage their equations and latex code in a personal workspace, which enables users to create new equations (with latex code, personal tags), edit and save equations. It also creates a URL of your equation, which can be shared to the collaborators. In the following sections, we will give you the step by step instructions on how to create, edit and share an equation. Once you finish add the equation, you can copy the latex code of your equation to clipboard and paste the code to your preferred latex file system, e.g. Overleaf, etc.

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).

Cheatsheet of Latex Code for Most Popular Natural Language Processing Equations

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).

In this blog, we will introduce how to use ChatGPT stock analysis chatbot to quickly generate professional charts of financial data. A few AI techniques such as RAG (Retrieval Augmented Generation), Text2SQL, and Larget Language Models (LLM) are used to help users find financial data, draw chart to compare key metrics, and adjust the color and shape of the chart using simple natural language prompts. A typical scenrio for professional financial analysts is to draw chart to compare the market capitalization, PE ratio of stocks from global market in converted currencies (US Dollars for example). This is a frequent scenario of daily workflow. The snapshots of AI generated charts is shown using DeepNLP Financial Assistant. You can try by clicking the button below the page, copy and paste the query in the chatbox and ask AI to generate charts or tables in few seconds, copy the charts to your reports or send to your manager. From the chart, we can see that on January 15th, Microsoft (MSFT) market cap reaches 2,887 Billon US dollars, and Apple (AAPL) reaches 2,874.68 Billion US dollars. Microsoft's market cap surpassed Apple and became the company of largest market capitalization in United States. The real-time stock data is from the web APIs, and converted to SQL using Text2SQL and LLM techniques.

研究如何打通tensorflow线下python脚本训练建模，线上生产环境用C++代码直接调用预先训练好的模型完成预测的工作，而不需要用自己写的Inference的函数。因为目前tensorflow提供的C++的API比较少，所以参考了几篇已有的日志，踩了不少坑一并记录下来。写了一个简单的ANN模型对Iris数据集分类的Demo。

Dialogue Agent Multimodal Visualization Tools for AI Systems: A Review. With the rapid development of generative AI technology, developing Chatbot Assistant, Dialogue and Agents have become frequent tasks for designer, developer, product manager, machine learning and AI practitioner. There are a lot data (usually in json format) exchange during different phases of prototyping, designing and developing modern AI systems, such as Chatbot Assistant, Agent, Dialogue, AI Image Generator, text to image, AI video generator, etc. There are potential needs to help visualize the data (json format or others) for AI system. In this blog, we will introduce you an online dialogue data visualization tool DeepNLP Dialogue Visualization to help visualize the agents and dialogue history with simply a json string data. And we will use the OpenAI Dialogue GPT model for a multi-turn dialogue developing for example.

In this blog, we will introduce how to use ChatGPT stock analysis chatbot to quickly generate professional charts of financial data. A few AI techniques such as RAG (Retrieval Augmented Generation), Text2SQL, and Larget Language Models (LLM) are used to help users find financial data, draw chart to compare key metrics, and adjust the color and shape of the chart using simple natural language prompts. A typical scenrio for professional financial analysts is to draw chart to compare the market capitalization, PE ratio of stocks from global market in converted currencies (US Dollars for example). This is a frequent scenario of daily workflow. The snapshots of AI generated charts is shown using DeepNLP Financial Assistant. You can try by clicking the button below the page, copy and paste the query in the chatbox and ask AI to generate charts or tables in few seconds, copy the charts to your reports or send to your manager. From the chart, we can see that on January 15th, Microsoft (MSFT) market cap reaches 2,887 Billon US dollars, and Apple (AAPL) reaches 2,874.68 Billion US dollars. Microsoft's market cap surpassed Apple and became the company of largest market capitalization in United States. The real-time stock data is from the web APIs, and converted to SQL using Text2SQL and LLM techniques.