In this blog, we will summarize the latex code for most popular machine learning equations, including multiple distance measures, generative models, etc. There are various distance measurements of different data distribution, including KL-Divergence, JS-Divergence, Wasserstein Distance(Optimal Transport), Maximum Mean Discrepancy(MMD) and so on. We will provide the latex code for machine learning models in the following sections. We will also provide latex code of Generative Adversarial Networks(GAN), Variational AutoEncoder(VAE), Diffusion Models(DDPM) for generative models in the second section.

In this blog, we will give you a brief introduction of what are multimodal models and what can multimodal generative models accomplish. OpenAI just released their latest text-to-video multimodal generative model "SORA" in Feb, 2024 which becomes extremely popular. SORA can generate short videos of up to 1 minute's length. Before SORA, there are also many generative multi-modal models released by various companies, such as BLIP, BLIP2, FLAMINGO, FlaVA, etc. We will summarize a complete list of these time tested multi-modal generative models, introduce the model architures (text and image encoder), the training process, tasks, latex equation of loss functions, the Vision Language capabilities (such as text-to-image, text-to-video, text-to-audio, visual question answering), etc. Tag: Multimodal, AIGC, Large Language Model

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 summarize the latex code for equations and formula sheet of CFA Level II exam, Formula Sheet Equations and Latex Code. Topics include QUANTITATIVE METHODS, such as LINEAR REGRESSION, MULTIPLE REGRESSION, TIME SERIES ANALYSIS, Machine Learning, ECONOMICS, Exchange rate, Covered interest rate parity, Uncovered interest rate parity, Relative purchasing power parity, Fisher and international Fisher effects, FX carry trade, Mundell-Fleming model, ECONOMIC GROWTH, Growth accounting equation, Labor productivity growth accounting equation, Classical growth model (Malthusian model), Neoclassical growth model (Solow’s model), Endogenous growth model, Convergence, ECONOMICS OF REGULATION. The data source of this blog is summarized from WILEY’S CFA PROGRAM LEVEL II quicksheet.

In this blog, we will introduce popular AI Agent Frameworks, Benchmarks (keep updated and beyond) Types and provide you some examples with Project Name, Project Website and its application and industries. The resources are collected from AI and ML websites and communities (github, huggingface, paper arxiv,etc) and the comprehensive will keep updating. You can also visit AI Agent Search to find the best resources AI Agents from various industries and applications. For AI Agent Frameworks, we will cover some popular AI agent frameworks, including LangChain, AutoGen, Crew AI etc. And for various types of AI agents, since it's very broad concepts, we will mainly cover the AI agents classified by Autonomous Ability (Auto AI Agents or Rule based) and by industries perspective. For AI Agent Benchmarks, this blog is usefully for AI and ML practitioners and beginners who want to understand what are AI Agents Benchmarks or Environments, the key capability why there are important and how the applications of these AI Agent benchmarks. We will cover different categories of AI Agent Environments, including Game-Based Environments, Text Chat-Based Environments, Physics and Robotics Simulations, Multi-Agent Platforms. Additionally, we can cover AI-Agents in various domains, such as the benchmarks and environments of AI Agents in Healthcare, AI Agents in Finance, AI Agents in Law, AI Agents in Education, etc. To find best AI Agent and Apps Search Engine and Navigation, please visit AI Agent Search.

Do you think robotics industry is promising in the new decade and why ? Please provide your reasons with evidences.

Please leave your vote for the most popular faces that your Humanoid AI robot wife would look like?Reminders: The answers should be human females.Related Post:https://www.quora.com/Is-a-robotic-wife-better-than-a-human-wife

Most recently I read Isaac Asimov's science fiction novels and know about his famous "Three Laws of Robotics" (https://en.wikipedia.org/wiki/Three_Laws_of_Robotics). But I am not so convinced by his first rule "A robot may not injure a human being", especially when AI reaches status of Artificial General intelligence (AGI) or even Artificial Super intelligence (ASI). What if in the near future, among a group of AIs, some of them follow this no-harm rule but the rest don't? I am curious about what's the probability these AIs reach a consensus to start harming people? I would like to build some mathmetical/probability/simulation models to best fit the scenario and calculate the probability. Any thoughts or discussion will be very welcome.

Hi, I am a developer who have some basic knowledge of AI Agent, RAG and Multi-Agent Dialogues. Now I have a small project on hand, which I need to build a AI Agent on the finance industry to give some realtime information to my clients. When I am choosing different AI Agent platforms, I have some difficulties. Right now, I am comparing among Google Vertex AI Agent Builder, Microsoft Azure AI Agents and Salesforce AI Agents. Any suggestions or some free AI Agent Builder recommendations?I am actually cost-sensitive, for example, Google Vertex AI Agent builders have price is $12 per 1,000 queries, and Vertex AI search is $2 per 1,000 queries, which is a little bit above my budget limit.

In this blog, we will introduce popular AI Agent Frameworks, Benchmarks (keep updated and beyond) Types and provide you some examples with Project Name, Project Website and its application and industries. The resources are collected from AI and ML websites and communities (github, huggingface, paper arxiv,etc) and the comprehensive will keep updating. You can also visit AI Agent Search to find the best resources AI Agents from various industries and applications. For AI Agent Frameworks, we will cover some popular AI agent frameworks, including LangChain, AutoGen, Crew AI etc. And for various types of AI agents, since it's very broad concepts, we will mainly cover the AI agents classified by Autonomous Ability (Auto AI Agents or Rule based) and by industries perspective. For AI Agent Benchmarks, this blog is usefully for AI and ML practitioners and beginners who want to understand what are AI Agents Benchmarks or Environments, the key capability why there are important and how the applications of these AI Agent benchmarks. We will cover different categories of AI Agent Environments, including Game-Based Environments, Text Chat-Based Environments, Physics and Robotics Simulations, Multi-Agent Platforms. Additionally, we can cover AI-Agents in various domains, such as the benchmarks and environments of AI Agents in Healthcare, AI Agents in Finance, AI Agents in Law, AI Agents in Education, etc. To find best AI Agent and Apps Search Engine and Navigation, please visit AI Agent Search.

In this blog, we will give you a brief introduction to AgentBoard, an open source AI agent visualization toolkit which can be used the same way as Tensorboard, which helps visaulize Tensors data during model training. AgentBoard aims to visualize key elements and information of AI Agents running loops in development and production stages of real-world scenarios. It provides easy logging APIs in python and can help log multiple data types such as text (prompt), tools use (function calling of LLM API), image, audio and video. With organized logging schemas, agentboard will visualize the agent-loop (PLAN -> ACT -> REFLECT -> REACT etc ), RAG (Retrieval Augumented Generation), Autonomous Agents running, Multi-Agents orchestration, etc. The rest of the blog will cover the basic data types usage, visualization of Agent Loop, Chat History, Tool Use, Functions Calling, RAG, etc.

大模型LLM 应用+AI Agents框架，为我们提供了非常便利的自动化执行任务的能力。微信公众号(订阅号)则是非常适合落地各种AI Agents的场景，我们可以利用微信公众号提供的文本、图像、语音的输入，在自己服务器上部署一套API框架，把自己感兴趣的一些对话、图文、语音等能力的API封装为Agents。这里给大家介绍一个拆箱即用的微信公众号服务端框架 Flask+tencent代码库来实现，并且会利用一个简单的金融智能助理(Finance Agent)的例子来实现一个根据用户输入来查询实时股价，并且返回给微信公众号用户的功能，支持更加复杂定制的AI Agents业务逻辑。

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.

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.

Most recently I read Isaac Asimov's science fiction novels and know about his famous "Three Laws of Robotics" (https://en.wikipedia.org/wiki/Three_Laws_of_Robotics). But I am not so convinced by his first rule "A robot may not injure a human being", especially when AI reaches status of Artificial General intelligence (AGI) or even Artificial Super intelligence (ASI). What if in the near future, among a group of AIs, some of them follow this no-harm rule but the rest don't? I am curious about what's the probability these AIs reach a consensus to start harming people? I would like to build some mathmetical/probability/simulation models to best fit the scenario and calculate the probability. Any thoughts or discussion will be very welcome.

In this blog, we will introduce popular AI Agent Frameworks, Benchmarks (keep updated and beyond) Types and provide you some examples with Project Name, Project Website and its application and industries. The resources are collected from AI and ML websites and communities (github, huggingface, paper arxiv,etc) and the comprehensive will keep updating. You can also visit AI Agent Search to find the best resources AI Agents from various industries and applications. For AI Agent Frameworks, we will cover some popular AI agent frameworks, including LangChain, AutoGen, Crew AI etc. And for various types of AI agents, since it's very broad concepts, we will mainly cover the AI agents classified by Autonomous Ability (Auto AI Agents or Rule based) and by industries perspective. For AI Agent Benchmarks, this blog is usefully for AI and ML practitioners and beginners who want to understand what are AI Agents Benchmarks or Environments, the key capability why there are important and how the applications of these AI Agent benchmarks. We will cover different categories of AI Agent Environments, including Game-Based Environments, Text Chat-Based Environments, Physics and Robotics Simulations, Multi-Agent Platforms. Additionally, we can cover AI-Agents in various domains, such as the benchmarks and environments of AI Agents in Healthcare, AI Agents in Finance, AI Agents in Law, AI Agents in Education, etc. To find best AI Agent and Apps Search Engine and Navigation, please visit AI Agent Search.

In this blog, we will introduce most popuplar formulas in Optics, Physics. We will also provide latex code of the equations. Topics include Bending of light, Snell's law, Fermat's principle, Paraxial geometrical, Mirrors, Magnification, Matrix Methods, Reflection and Transmission, Fresnel Equations, Polarization of Optics, Prisms and Dispersion, Diffraction, Birefringence and Dichroism, Retarders: waveplates and compensators, Fabry-Perot interferometer, etc.

In this blog, we will introduce most popuplar formulas in Oscillations, Physics. We will also provide latex code of the equations. Topics include harmonic oscillations, mechanic oscillations, electric oscillations, waves in long conductors, coupled conductors and transformers, pendulums, harmonic wave, etc.

In this blog, we will introduce most popuplar formulas in Quantum Physics. We will also provide latex code of the equations. Topics of Quantum Physics include Black Body Radiation, The Compton Effect, Quantum Wave Functions, Operators in Quantum Physics, The Uncertainty Principle, The Schrödinger Equation, Parity, The Tunnel Effect, Harmonic Oscillator, Angular momentum, Spin, the Dirac Formalism, and so on.

In this blog, we will summarize the latex code of most popular formulas and equations for Microeconomics, Economics. We will cover important topics, including Allocative Efficiency Condition, Annuities Due, Average Fixed Cost, Average Product, Average Revenue, Average Total Cost, Average Variable Cost ,Compound Interest, Cross-Price Elasticity of Demand, Effective Rate, Elasticity of Supply, Factor of Production Hiring Rule, Future Value of Ordinary Annuities Marginal Cost, Marginal Revenue Product, Present Value of Ordinary Annuities, Simple Interest ,Distributive Efficiency Condition, Gini Coefficient, Marginal Factor Cost MFC, Marginal Product of Labor, Marginal Revenue, Marginal Revenue Product of Labor MRPL, Optimal Combination of Resources Condition, Optimal Consumption Rule, Price Elasticity of Demand, Price for a Competitive Firm, Production Efficiency Condition, Profit, Profit-Maximizing Output Level, Slope of the Total Product Curve, Socially Optimal Level of Output, Total Costs, etc.

In this blog, we will summarize the latex code for equations of CFA Level I exam, Formula Sheet Equations and Latex Code, and provide Chatbot as AI Assistant to facilitate your reading. You can ask question like what is "Real GDP" in the chatbox. Topics in the blog include three major parts of CFA Level I exam: QUANTITATIVE, ECONOMICS and FINANCIAL REPORTING. Detailed topics include THE TIME VALUE OF MONEY, Future Value, Present Value, Effective Annual Rate, Continuous Compounding, Ordinary Annuity, Annuity Due, Perpetuity, STATISTICAL CONCEPT AND MARKET RETURNS, Fisher Skewness, Kurtosis, Two-asset portfolio, Three-asset portfolio, Microeconomics, Simple Interest, Effective Rate, Future Value of Ordinary Annuities, Annuities Due, Present Value of Ordinary Annuities, Allocative Efficiency Condition, Average Fixed Cost; Macroeconomics Investment, Aggregate Expenditure Without Government or Foreign Sectors, Marginal Propensity to Consume MPC, Marginal Propensity Save MPS, Sum of Marginal Propensity to Save and Marginal Propensity to Consume, Autonomous Spending Multiplier, Balanced Budget Multiplier, Banks Reserve Ratio, Nominal Interest Rate, Real GDP, Real Interest Rate, Tax Multiplier, Unemployment Rate. FINANCIAL REPORTING and ANALYSIS, Basic EPS, Diluted EPS, Balance Sheet, Free Cash Flow to the Firm, Cash Flow Performance Ratio, Cash Flow To Revenue Ratio, Cash Return On Assets, Cash Return On Assets, Cash Return On Equity, Activity Ratio, Inventory Turnover, Days of Inventory On Hand (DOH), Receivables Turnover, Days of Sales Outstanding, etc.

In this blog, we will summarize the latex code for equations and formula sheet of CFA Level II exam, Formula Sheet Equations and Latex Code. Topics include QUANTITATIVE METHODS, such as LINEAR REGRESSION, MULTIPLE REGRESSION, TIME SERIES ANALYSIS, Machine Learning, ECONOMICS, Exchange rate, Covered interest rate parity, Uncovered interest rate parity, Relative purchasing power parity, Fisher and international Fisher effects, FX carry trade, Mundell-Fleming model, ECONOMIC GROWTH, Growth accounting equation, Labor productivity growth accounting equation, Classical growth model (Malthusian model), Neoclassical growth model (Solow’s model), Endogenous growth model, Convergence, ECONOMICS OF REGULATION. The data source of this blog is summarized from WILEY’S CFA PROGRAM LEVEL II quicksheet.

In this blog, we will summarize the latex code for equations of CFA Level I exam, Formula Sheet Equations and Latex Code, and provide Chatbot as AI Assistant to facilitate your reading. You can ask question like what is "Real GDP" in the chatbox. Topics in the blog include three major parts of CFA Level I exam: QUANTITATIVE, ECONOMICS and FINANCIAL REPORTING. Detailed topics include THE TIME VALUE OF MONEY, Future Value, Present Value, Effective Annual Rate, Continuous Compounding, Ordinary Annuity, Annuity Due, Perpetuity, STATISTICAL CONCEPT AND MARKET RETURNS, Fisher Skewness, Kurtosis, Two-asset portfolio, Three-asset portfolio, Microeconomics, Simple Interest, Effective Rate, Future Value of Ordinary Annuities, Annuities Due, Present Value of Ordinary Annuities, Allocative Efficiency Condition, Average Fixed Cost; Macroeconomics Investment, Aggregate Expenditure Without Government or Foreign Sectors, Marginal Propensity to Consume MPC, Marginal Propensity Save MPS, Sum of Marginal Propensity to Save and Marginal Propensity to Consume, Autonomous Spending Multiplier, Balanced Budget Multiplier, Banks Reserve Ratio, Nominal Interest Rate, Real GDP, Real Interest Rate, Tax Multiplier, Unemployment Rate. FINANCIAL REPORTING and ANALYSIS, Basic EPS, Diluted EPS, Balance Sheet, Free Cash Flow to the Firm, Cash Flow Performance Ratio, Cash Flow To Revenue Ratio, Cash Return On Assets, Cash Return On Assets, Cash Return On Equity, Activity Ratio, Inventory Turnover, Days of Inventory On Hand (DOH), Receivables Turnover, Days of Sales Outstanding, etc.

In this blog, we will summarize the latex code for equations and formula sheet of CFA Level II exam, Formula Sheet Equations and Latex Code. Topics include QUANTITATIVE METHODS, such as LINEAR REGRESSION, MULTIPLE REGRESSION, TIME SERIES ANALYSIS, Machine Learning, ECONOMICS, Exchange rate, Covered interest rate parity, Uncovered interest rate parity, Relative purchasing power parity, Fisher and international Fisher effects, FX carry trade, Mundell-Fleming model, ECONOMIC GROWTH, Growth accounting equation, Labor productivity growth accounting equation, Classical growth model (Malthusian model), Neoclassical growth model (Solow’s model), Endogenous growth model, Convergence, ECONOMICS OF REGULATION. The data source of this blog is summarized from WILEY’S CFA PROGRAM LEVEL II quicksheet.

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.

Hi parents and kids, this free online AI course "Use generative AI to Draw Paw Patrol Dog Chase" will gives your K12 kids a brief introduction to what is Artificial Intelligence (AI) technology, what can AI accomplish (such as AI Generated Contents converting text to image, Chatbots giving automatic response), and how does AI work in the easiest way that even K12 children can understand. This course covers some fundamental concepts of AI, including what neural networks is, what's the most popular neural network blocks of AI models (Transformers, Diffusion, etc.). Finally, kids can also participate by interacting with the AI during the course, by clicking the image or text with 'robot' icon or button, and get more detailed explanation, type a query or ask question using microphone by themselves, and accomplish the task of generating a picture of unique Paw Patrol Dogs and get the trophy. This course will takes around 20 minutes to complete. And all the interactive learning sessions in the course are designed by experienced children-education professionals. Let's get started!

Hi parents and kids, this free online AI course "Use generative AI to Draw Paw Patrol Dog Skye" will gives your K12 kids a brief introduction to what is Artificial Intelligence (AI) technology, what can AI accomplish (such as AI Generated Contents converting text to image, Chatbots giving automatic response), and how does AI work in the easiest way that even K12 children can understand. This course covers some fundamental concepts of AI, including what neural networks is, what's the most popular neural network blocks of AI models (Transformers, Diffusion, etc.). Finally, kids can also participate by interacting with the AI during the course, by clicking the image or text with 'robot' icon or button, and get more detailed explanation, type a query or ask question using microphone by themselves, and accomplish the task of generating a picture of unique Paw Patrol Dogs and get the trophy. This course will takes around 20 minutes to complete. And all the interactive learning sessions in the course are designed by experienced children-education professionals. Let's get started!

Please leave your thoughts in the comments

Please leave your thoughts and show me some videos or images

Hi parents and kids, this free online AI course "Use generative AI to Draw Paw Patrol Dog Chase" will gives your K12 kids a brief introduction to what is Artificial Intelligence (AI) technology, what can AI accomplish (such as AI Generated Contents converting text to image, Chatbots giving automatic response), and how does AI work in the easiest way that even K12 children can understand. This course covers some fundamental concepts of AI, including what neural networks is, what's the most popular neural network blocks of AI models (Transformers, Diffusion, etc.). Finally, kids can also participate by interacting with the AI during the course, by clicking the image or text with 'robot' icon or button, and get more detailed explanation, type a query or ask question using microphone by themselves, and accomplish the task of generating a picture of unique Paw Patrol Dogs and get the trophy. This course will takes around 20 minutes to complete. And all the interactive learning sessions in the course are designed by experienced children-education professionals. Let's get started!

Hi parents and kids, this free online AI course "Use generative AI to Draw Paw Patrol Dog Skye" will gives your K12 kids a brief introduction to what is Artificial Intelligence (AI) technology, what can AI accomplish (such as AI Generated Contents converting text to image, Chatbots giving automatic response), and how does AI work in the easiest way that even K12 children can understand. This course covers some fundamental concepts of AI, including what neural networks is, what's the most popular neural network blocks of AI models (Transformers, Diffusion, etc.). Finally, kids can also participate by interacting with the AI during the course, by clicking the image or text with 'robot' icon or button, and get more detailed explanation, type a query or ask question using microphone by themselves, and accomplish the task of generating a picture of unique Paw Patrol Dogs and get the trophy. This course will takes around 20 minutes to complete. And all the interactive learning sessions in the course are designed by experienced children-education professionals. Let's get started!

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.

In this blog, we will summarize the latex code for equations of CFA Level I exam, Formula Sheet Equations and Latex Code, and provide Chatbot as AI Assistant to facilitate your reading. You can ask question like what is "Real GDP" in the chatbox. Topics in the blog include three major parts of CFA Level I exam: QUANTITATIVE, ECONOMICS and FINANCIAL REPORTING. Detailed topics include THE TIME VALUE OF MONEY, Future Value, Present Value, Effective Annual Rate, Continuous Compounding, Ordinary Annuity, Annuity Due, Perpetuity, STATISTICAL CONCEPT AND MARKET RETURNS, Fisher Skewness, Kurtosis, Two-asset portfolio, Three-asset portfolio, Microeconomics, Simple Interest, Effective Rate, Future Value of Ordinary Annuities, Annuities Due, Present Value of Ordinary Annuities, Allocative Efficiency Condition, Average Fixed Cost; Macroeconomics Investment, Aggregate Expenditure Without Government or Foreign Sectors, Marginal Propensity to Consume MPC, Marginal Propensity Save MPS, Sum of Marginal Propensity to Save and Marginal Propensity to Consume, Autonomous Spending Multiplier, Balanced Budget Multiplier, Banks Reserve Ratio, Nominal Interest Rate, Real GDP, Real Interest Rate, Tax Multiplier, Unemployment Rate. FINANCIAL REPORTING and ANALYSIS, Basic EPS, Diluted EPS, Balance Sheet, Free Cash Flow to the Firm, Cash Flow Performance Ratio, Cash Flow To Revenue Ratio, Cash Return On Assets, Cash Return On Assets, Cash Return On Equity, Activity Ratio, Inventory Turnover, Days of Inventory On Hand (DOH), Receivables Turnover, Days of Sales Outstanding, etc.

Hi parents and kids, this free online AI course "Use generative AI to Draw Paw Patrol Dog Chase" will gives your K12 kids a brief introduction to what is Artificial Intelligence (AI) technology, what can AI accomplish (such as AI Generated Contents converting text to image, Chatbots giving automatic response), and how does AI work in the easiest way that even K12 children can understand. This course covers some fundamental concepts of AI, including what neural networks is, what's the most popular neural network blocks of AI models (Transformers, Diffusion, etc.). Finally, kids can also participate by interacting with the AI during the course, by clicking the image or text with 'robot' icon or button, and get more detailed explanation, type a query or ask question using microphone by themselves, and accomplish the task of generating a picture of unique Paw Patrol Dogs and get the trophy. This course will takes around 20 minutes to complete. And all the interactive learning sessions in the course are designed by experienced children-education professionals. Let's get started!

Hi parents and kids, this free online AI course "Use generative AI to Draw Paw Patrol Dog Skye" will gives your K12 kids a brief introduction to what is Artificial Intelligence (AI) technology, what can AI accomplish (such as AI Generated Contents converting text to image, Chatbots giving automatic response), and how does AI work in the easiest way that even K12 children can understand. This course covers some fundamental concepts of AI, including what neural networks is, what's the most popular neural network blocks of AI models (Transformers, Diffusion, etc.). Finally, kids can also participate by interacting with the AI during the course, by clicking the image or text with 'robot' icon or button, and get more detailed explanation, type a query or ask question using microphone by themselves, and accomplish the task of generating a picture of unique Paw Patrol Dogs and get the trophy. This course will takes around 20 minutes to complete. And all the interactive learning sessions in the course are designed by experienced children-education professionals. Let's get started!

In this blog, we will summarize the latex code for equations and formula sheet of CFA Level II exam, Formula Sheet Equations and Latex Code. Topics include QUANTITATIVE METHODS, such as LINEAR REGRESSION, MULTIPLE REGRESSION, TIME SERIES ANALYSIS, Machine Learning, ECONOMICS, Exchange rate, Covered interest rate parity, Uncovered interest rate parity, Relative purchasing power parity, Fisher and international Fisher effects, FX carry trade, Mundell-Fleming model, ECONOMIC GROWTH, Growth accounting equation, Labor productivity growth accounting equation, Classical growth model (Malthusian model), Neoclassical growth model (Solow’s model), Endogenous growth model, Convergence, ECONOMICS OF REGULATION. The data source of this blog is summarized from WILEY’S CFA PROGRAM LEVEL II quicksheet.

Please leave your thoughts on the best and coolest AI Generated Images.

Please leave your thoughts on free alternatives to Midjourney Stable Diffusion and other AI Image Generators.

Please leave your thoughs on the most scary or creepiest AI Generated Images.

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.

We are seeing more applications of robotaxi and self-driving vehicles worldwide. Many large companies such as Waymo, Tesla and Baidu are accelerating their speed of robotaxi deployment in multiple cities. Some human drivers especially cab drivers worry that they will lose their jobs due to AI. They argue that the lower operating cost and AI can work technically 24 hours a day without any rest like human will have more competing advantage than humans. What do you think?

Please leave your thoughts on whether human artists will be replaced by AI Image Generator. Some similar posts on other platforms including quora and reddit. Is art even worth making anymore, Will AI art eventually permanently replace human artists, Do you think AI will ever replace artists, Do people really think that replacing artists with ai is a good idea

Please leave your thought on the typical use scenarios of four-legged Quadruped Robot Dogs.Related Post:
https://www.quora.com/What-are-the-most-common-business-use-cases-for-robot-dogs

Who is your favourite pick and please provide some reasons?

Please leave your thoughts on the most import features Humanoid Robot should have in the future.Related Post:
https://www.quora.com/What-features-will-your-humanoid-robot-need-to-have-in-the-future

In this blog, we will summarize the latex code of most fundamental equations of multi-task learning(MTL) and transfer learning(TL). Multi-Task Learning aims to optimize N related tasks simultaneously and achieve the overall trade-off between multiple tasks. Typical network structure include shared-bottom models, Cross-Stitch Network, Multi-Gate Mixture of Experts (MMoE), Progressive Layered Extraction (PLE), Entire Space Multi-Task Model (ESSM) models and etc. Different from multi-task learning. In the following sections, we will dicuss more details of MTL equations, which is useful for your quick reference.

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 most fundamental equations of Few-Shot Learning and Zero-Shot Learning. Few-Shot Learning learns from a few-labelled examples and better generalize to unseen examples. Typical works includes Prototypical Networks, Model-Agnostic Meta-Learning (MAML), 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 will summarize the latex code of most fundamental and popular knowledge graph (KG) Equations, with special focus on the link prediction tasks. We will cover a wide range of models, including TransE, TransR, TransE, RotatE, SME(Linear), SimplE etc. Knowledge Graph consists of a set of triples [(h, r, t)]. h and t denotes the head node and tail node respectively. And r denotes multiple relation types. One common solution to the link prediction tasks is to learn low-dimensional embeddings of entities(E) and relations (R), and infer the missing part of [(?, r, t), (h, ?, t), (h, r, ?)].

In this blog, we will summarize the latex code for complex variables formulas, including complex numbers, De Moivreâ??s theorem and power series for complex variables e^{z}, sin(z), cos(z), ln(1+z), (1+z)^{n}, etc.

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 how simple it is to write a python spider to crawl realtime stock quotes from Hong Kong Stock Market (HKEX) official website. We will use the common python lib "requests" and "BeautifulSoup". And the complete code is also attached to this blog. We use Tencent(stock code: 700) as an example to show you how to to download the realtime stock price quote from HKEX's official website

We are seeing more applications of robotaxi and self-driving vehicles worldwide. Many large companies such as Waymo, Tesla and Baidu are accelerating their speed of robotaxi deployment in multiple cities. Some human drivers especially cab drivers worry that they will lose their jobs due to AI. They argue that the lower operating cost and AI can work technically 24 hours a day without any rest like human will have more competing advantage than humans. What do you think?

We are seeing more applications of robotaxi and self-driving vehicles worldwide. Many large companies such as Waymo, Tesla and Baidu are accelerating their speed of robotaxi deployment in multiple cities. Some human drivers especially cab drivers worry that they will lose their jobs due to AI. They argue that the lower operating cost and AI can work technically 24 hours a day without any rest like human will have more competing advantage than humans. What do you think?