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 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.

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.

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.

In this blog, we will summarize the latex code of most popular kernel methods and Gaussian Process models, including Support Vector Machine (SVM), Gaussian Process (GP) and Deep Kernel Learning(DKL).

In this blog, we will summarize the latex code of most fundamental equations of GPT (Generative Pre-trained Transformer) Models. This blog will cover many popular GPT models, including GPT1, GPT2, GPT3, GPT4, InstructGPT, ChatGPT, etc.

In this blog, we will introduce 4 ways to use ChatGPT powered chatbot to gain real-time information of global stock markets and accelerate your working productivity. The global financial stock market's realtime data is usually seperated across different websites globally, including NASDAQ, NYSE, London Stock Change(LSE), Hongkong Stock Exchange(HKEX), Tokyo stock Exchange(TSE), National Stock Exchange in India (NSE), SHANGHAI/SHENZHEN stock Exchange, etc. Use ChatGPT powered Chatbot can help query stock prices from diffent market, compare them in a single question in the natural languange manner. For example, you can ask Chatbot a question: "Stock price of Tesla, Shell and Alibaba" in a single question. You can gain also more information from there different global markets at the same time (NASDAQ, London Stock Exchange and Hongkong Stock Exchange, etc). Additionally, you can also ask more specific questions like financial data including PE ratio, Market Capitalization (Market Cap), High Price of a particular stocks. Comparing the financial data performance, such as stock price, market cap, pe ratio of multiple stocks are also a frequent way of using Chatbot. Also you can ask the chatbot to help you plot a table or chart to compare the stocks, which you can just copy and paste to your daily financial report.

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.

In this blog, we will show you 3 easy steps to create your personal financial chatbot assistant powered by ChatGPT. ChatGPT is Large Language Model (LLM) based Artificial Intelligent service created by OpenAI company, who just released their latest Assistant API for chatbot creation. These AI assistants are very useful in financial industries. Common use cases include: Generating realtime stock price quotes, Analyzing financial data, Providing investment advices, generating summary of financial reports, etc. Keywords: Financial Chatbot, Financial ChatGPT, Chatbot Stock Price, Chatbot NYSE, Chatbot.

In this blog, we will summarize the latex code of equations for Diffusion Models, which are among the top-performing generative models, including GAN, VAE and flow-based models. The basic idea of diffusion models are to inject random noise to the feature vector in the forward process as markov chain models, and in the reverse process gradualy reconstruct the feature vector for generation. See below blogpost as reference for more details: Weng, Lilian. (Jul 2021). What are diffusion models? Lilâ??Log. lilianweng.github.io/posts/2021-07-11-diffusion-models/

利用tensorflow训练深度神经网络模型需要消耗很长时间，因为并行化计算就为提升运行速度提供了重要思路。Tensorflow提供了多种方法来使程序的并行运行，在使用这些方法时需要考虑的问题有：选取的计算设备是CPU还是GPU，每个CPU多少核的资源并行计算，构建图Graph时消耗资源如何分配等等问题。下面我们以Linux多核CPU的环境为例介绍几种常见方法来提升你的tensorflow程序的运行速度。