Tag: OpenAI

Chatting with your Enterprise data privately and securely through the use of Azure Cognitive Search and Azure Open AI

In an age where data is power, businesses are constantly looking for ways to leverage their vast enterprise data stores. One promising avenue lies in the intersection of AI and search technologies, specifically through the use of Azure Cognitive Search and Azure Open AI. These tools provide powerful ways to converse with enterprise data privately and securely.

Enterprise data can take various forms, from structured database datasets to unstructured documents, emails, and files. Some examples are data about the company’s benefits, internal policies, job descriptions, roles, and much more.

What is Azure Cognitive Search?

Azure Cognitive Search is a cloud-based service provided by Microsoft Azure that enables developers to build sophisticated search experiences into custom applications. It integrates with other Azure Cognitive Services to enable AI-driven content understanding through capabilities such as natural language processing, entity recognition, image analysis, and more.

Here are some of the key benefits of Azure Cognitive Search:

  1. Fully Managed: Azure Cognitive Search is fully managed, meaning you don’t have to worry about infrastructure setup, maintenance, or scaling. You just need to focus on the development of your application.
  2. Rich Search Experiences: It allows for the creation of rich search experiences, including auto-complete, geospatial search, filtering, and faceting.
  3. AI-Enhanced Search Capabilities: When combined with other Azure Cognitive Services, Azure Cognitive Search can provide advanced search features. For example, it can extract key phrases, detect languages, identify entities, and more. It can even index and search unstructured data, like text within documents or images.
  4. Scalability and Performance: Azure Cognitive Search can automatically scale to handle large volumes of data and high query loads. It provides fast, efficient search across large datasets.
  5. Data Integration: It can pull in data from a variety of sources, including Azure SQL Database, Azure Cosmos DB, Azure Blob Storage, and more.
  6. Security: Azure Cognitive Search supports data encryption at rest and in transit. It also integrates with Azure Active Directory for identity and access management.
  7. Developer Friendly: It provides a simple, RESTful API and integrates with popular programming languages and development frameworks. This makes it easier for developers to embed search functionality into applications.
  8. Indexing: The service provides robust indexing capabilities, allowing you to index data from a variety of sources and formats. This allows for a more comprehensive search experience for end-users.

In summary, Azure Cognitive Search can provide powerful, intelligent search capabilities for your applications, allowing users to find the information they need quickly and easily.

What is Azure Open AI?

Azure OpenAI Service is a platform that provides REST API access to OpenAI’s powerful language models, including GPT-3, GPT-4, Codex, and Embeddings. It can be used for tasks such as content generation, summarization, semantic search, and natural language-to-code translation.

The security and safety of enterprise data is a top priority for Azure OpenAI. Here are some key points on how it ensures safety:

  • The Azure OpenAI Service is fully controlled by Microsoft and does not interact with any services operated by OpenAI. Your prompts (inputs) and completions (outputs), your embeddings, and your training data are not available to other customers, OpenAI, or used to improve OpenAI models, any Microsoft or 3rd party products or services, or to automatically improve Azure OpenAI models for your use in your resource. Your fine-tuned Azure OpenAI models are available exclusively for your use.
  • The service processes different types of data including prompts and generated content, augmented data included with prompts, and training & validation data.
  • When generating completions, images, or embeddings, the service evaluates the prompt and completion data in real-time to check for harmful content types. The models are stateless, meaning no prompts or generations are stored in the model, and prompts and generations are not used to train, retrain, or improve the base models.
  • With the “on your data” feature, the service retrieves relevant data from a configured data store and augments the prompt to produce generations that are grounded with your data. The data remains stored in the data source and location you designate. No data is copied into the Azure OpenAI service.
  • Training data uploaded for fine-tuning is stored in the Azure OpenAI resource in the customer’s Azure tenant. It can be double encrypted at rest and can be deleted by the customer at any time. This data is not used to train, retrain, or improve any Microsoft or 3rd party base models.
  • Azure OpenAI includes both content filtering and abuse monitoring features to reduce the risk of harmful use of the service. To detect and mitigate abuse, Azure OpenAI stores all prompts and generated content securely for up to thirty (30) days.
  • The data store where prompts and completions are stored is logically separated by customer resources. Prompts and generated content are stored in the Azure region where the customer’s Azure OpenAI service resource is deployed, within the Azure OpenAI service boundary. Human reviewers can only access the data when it has been flagged by the abuse monitoring system.
  • Customers who meet additional Limited Access eligibility criteria and attest to specific use cases can apply to modify the Azure OpenAI content management features. Suppose Microsoft approves a customer’s request to change abuse monitoring. In that case, Microsoft does not store any prompts and completions associated with the approved Azure subscription for which abuse monitoring is configured.

In conclusion, Azure OpenAI takes numerous measures to ensure that your enterprise data is kept secure and confidential while using its service.

Revolutionize your Enterprise Data with ChatGPT: step by step how to create your own Enterprise Chat

This sample demonstrates a few approaches for creating ChatGPT-like experiences over your own data using the Retrieval Augmented Generation pattern. It uses Azure Open AI Service to access the ChatGPT model (gpt-35-turbo), and Azure Cognitive Search for data indexing and retrieval.

The repo includes sample data so it’s ready to try end-to-end. In this sample application, we use a fictitious company called Contoso Electronics, and the experience allows its employees to ask questions about the benefits, internal policies, as well as job descriptions, and roles.

Features

  • Chat and Q&A interfaces
  • Explores various options to help users evaluate the trustworthiness of responses with citations, tracking of source content, etc.
  • Shows possible approaches for data preparation, prompt construction, and orchestration of interaction between model (ChatGPT) and retriever (Cognitive Search)
  • Settings directly in the UX to tweak the behavior and experiment with options
Chat screen

Getting Started

IMPORTANT: In order to deploy and run this example, you’ll need an Azure subscription with access enabled for the Azure OpenAI service. You can request access here. You can also visit here to get some free Azure credits to get you started.

AZURE RESOURCE COSTS by default this sample will create Azure App Service and Azure Cognitive Search resources that have a monthly cost, as well as Form Recognizer resource that has cost per document page. You can switch them to free versions of each of them if you want to avoid this cost by changing the parameters file under the infra folder (though there are some limits to consider; for example, you can have up to 1 free Cognitive Search resource per subscription, and the free Form Recognizer resource only analyzes the first 2 pages of each document.)

Prerequisites

To Run Locally

  • Azure Developer CLI
  • Python 3+
    • Important: Python and the pip package manager must be in the path in Windows for the setup scripts to work.
    • Important: Ensure you can run python --version from the console. On Ubuntu, you might need to run sudo apt install python-is-python3 to link python to python3.
  • Node.js
  • Git
  • Powershell 7+ (pwsh) – For Windows users only.
    • Important: Ensure you can run pwsh.exe from a PowerShell command. If this fails, you likely need to upgrade PowerShell.

NOTE: Your Azure Account must have Microsoft.Authorization/roleAssignments/write permissions, such as User Access Administrator or Owner.

Installation

Project Initialization

  1. Create a new folder and switch to it in the terminal
  2. Run azd auth login
  3. Run azd init -t azure-search-openai-demo
    • For the target location, the regions that currently support the models used in this sample are East US or South Central US. For an up-to-date list of regions and models, check here
    • note that this command will initialize a git repository and you do not need to clone this repository

Starting from scratch:

Execute the following command, if you don’t have any pre-existing Azure services and want to start from a fresh deployment.

  1. Run azd up – This will provision Azure resources and deploy this sample to those resources, including building the search index based on the files found in the ./data folder.
  2. After the application has been successfully deployed you will see a URL printed to the console. Click that URL to interact with the application in your browser.

For detailed information click here on my GitHub and follow a video from Microsoft talking about the example solution.

You can look at the Chat App that I’ve developed, which I will make available for you to test for a few days.

Firstly, it’s important to understand that you have the ability to replace the PDF files within the “./data” directory with your own business data.

If you wish to examine these files first to gain insights into the types of questions you can make in the chat to test, please click here.

Regrettably, the demo app had to be deactivated due to Azure expenses. If you’d like it to be reactivated, please click here to contact me. Thank you.

You’re able to query any content found within the enterprise PDF files located in the “./data” directory. The chat will respond with citations from the respective PDFs, and you have the option to click through and verify the information directly from the source PDF.

Conclusion

The vast universe of enterprise data, spanning from structured database datasets to unstructured documents, emails, and files, holds a wealth of insights that can drive an organization’s growth and success. Azure Cognitive Search and Azure OpenAI serve as powerful tools that make this data readily accessible, private, and secure. By leveraging these technologies, businesses can tap into the full potential of their internal data, from understanding the intricacies of their benefits and policies to defining roles and job descriptions more effectively. With a future powered by AI and machine learning, the conversations we can have with our data are only just beginning. This is more than just a technological shift; it’s a new era of informed decision-making, driven by data that’s within our reach. This solution provides an array of opportunities to assist businesses in leveraging their corporate data and disseminating it amongst their employees. This method simplifies comprehension, fostering organizational growth and enhancing the company culture. Should you require additional details on this topic, please do not hesitate to reach out to me.

That’s it for today!

AutoGPT: The Game Changer in Artificial Intelligence and Autonomous Agents

Auto-GPT is a revolutionary technology that unleashes new abilities for ChatGPT, enabling it to complete tasks all by itself, creating its own prompts to get the job done. AutoGPT, a groundbreaking artificial intelligence (AI) model, has taken the world by storm with its ability to provide large language models with “arms and hands” for task execution based on specific goals. This state-of-the-art technology has captured the attention of open-source developers and has the potential to revolutionize the AI landscape. For those who may not be familiar with AutoGPT, this article will provide an in-depth overview of this innovative AI model, its key features, and its impact on industries and applications.

The buzz around Auto-GPT has recently surpassed ChatGPT itself, trending as number one on Twitter for several days in a row.

How AutoGPT works?

AutoGPT works by utilizing the GPT-4 language model as its core intelligence to automate tasks and perform web searches. To use AutoGPT, you need to provide three inputs:

  1. AI Name: A name for the AI instance.
  2. AI Role: A description of the AI’s purpose.
  3. Up to 5 goals: Specific tasks you want the AI to accomplish.

Once these inputs are provided, AutoGPT starts working on the assigned goals. It may search the internet, extract information, or perform other necessary actions to complete the tasks.

AutoGPT also features long and short-term memory management, allowing it to learn from past experiences and make better decisions based on context. This is achieved through its integration with vector databases for memory storage. Additionally, unlike ChatGPT, AutoGPT has internet access, which enables it to fetch relevant information from the web as needed. Furthermore, it can manipulate files, access, and extract data from them, and summarize the information if required.

Follow 3 examples of how AutoGPT works:

1 – Market Research on Headphones: AI Name: ResearchGPT AI Role: An AI designed to conduct market research on tech products.

Goal 1: Do market research for different headphones on the market today. Goal 2: Get the top 5 headphones and list their pros and cons. Goal 3: Include the price of each one and save the analysis. Goal 4: Once you are done, terminate.

Auto-GPT will search the internet, find information on various headphones, list the top 5 headphones with their pros, cons, and prices, save the analysis, and terminate once the task is complete.

2 – Create FAQs for a Product: AI Name: FAQGPT AI Role: An AI designed to create FAQs for products.

Goal 1: Research a new smartphone model and its features. Goal 2: Create a list of 10 frequently asked questions about the smartphone. Goal 3: Provide clear and concise answers to the FAQs. Goal 4: Save the FAQs in a text file. Goal 5: Once you are done, terminate.

In this case, AutoGPT will research the specified smartphone model, create a list of FAQs, answer them, save the information in a text file, and terminate after completing the task.

3 – Writing a Python Program: AI Name: CodeGPT AI Role: An AI designed to write simple Python programs.

Goal 1: Write a Python program that calculates the factorial of a given number. Goal 2: Test the program with sample inputs and ensure it works correctly. Goal 3: Save the Python code in a .py file. Goal 4: Once you are done, terminate.

AutoGPT will generate the Python code to calculate the factorial of a given number, test it with sample inputs, save the code in a .py file, and terminate upon completion.

Keep in mind that AutoGPT might not always be perfect in completing the assigned tasks, as its performance depends on the accuracy and limitations of the GPT-4 model it is built upon.

AutoGPT boasts several key features that set it apart from its predecessors

  1. Dynamic learning: AutoGPT is designed to adapt to new data, making it an ever-evolving conversational AI model that stays up-to-date with the latest information and trends.
  2. Enhanced context awareness: AutoGPT’s understanding of context and user intent has been fine-tuned to provide more accurate and relevant responses.
  3. Customization capabilities: AutoGPT can be tailored to specific industries and applications, making it a versatile tool for many use cases.

AutoGPT’s Impact on Industries and Applications

The innovative features of AutoGPT are transforming various sectors through a wide range of applications:

  1. Personalized marketing: AutoGPT creates targeted marketing campaigns by continuously learning from user data and preferences.
  2. Sentiment analysis: AutoGPT accurately gauges user sentiment, providing valuable insights for businesses to improve customer experiences.
  3. Real-time adaptation: AutoGPT adapts to changing market conditions and trends, ensuring AI-powered solutions remain relevant and practical.
  4. Automation of complex tasks: AutoGPT’s self-improvement capabilities make it suitable for automating intricate tasks and streamlining processes across industries.

Integration of AutoGPT with advanced conversational AI models like BabyAGI, AgentGPT, and Microsoft’s Jarvis unlocks the full potential of AI and revolutionizes human-technology interactions. These AI models are transforming the world by enabling innovative applications across industries, such as enhanced customer support, improved content generation, seamless language translation, virtual personal assistants, healthcare applications, education and training, and human resources management.

AutoGPT also has a number of limitations, such as:

  1. Imperfect accuracy: AutoGPT is built upon the GPT-4 language model, which, although a significant improvement over GPT-3.5, is still not 100% accurate. Errors in the generated output might require further steps to resolve or could lead to an inability to complete the assigned task.
  2. Looping issues: While working on a task, AutoGPT may get stuck in a loop trying to find solutions to errors or problems. This can cause delays and increase costs, as the GPT-4 API usage fees can become expensive.
  3. Cost: The GPT-4 API, which Auto-GPT relies on, is more expensive than the GPT-3.5 API. The costs can quickly add up, especially if the AI is stuck in a loop or takes multiple steps to accomplish a task.
  4. Not production-ready: AutoGPT is not yet considered a production-ready solution. Users have reported that it often does not complete projects or only partially solves tasks. It requires further refinement and development before it can be relied upon as a complete, dependable solution.
  5. Task-specific limitations: AutoGPT might perform well for relatively simple and straightforward tasks, but it could struggle with more complex tasks or tasks requiring specialized knowledge. Its capabilities are limited by the underlying GPT-4 model and its ability to understand and solve a given problem.

These limitations should be taken into consideration when using AutoGPT, as it may not be suitable for all use cases or provide flawless results.

There are two methods for utilizing and evaluating AutoGPT

The first one is to download and install in our computer the AutoGPT source code from GitHub. Follow the instruction above. This maybe requires technical knowledge.

https://github.com/Significant-Gravitas/Auto-GPT/releases/latest

The second one involves utilizing a version I have deployed for direct access in your browser via this link and having fun!

You must input the name and goal, then click on “Deploy Agent.” Entering your OpenAI key is required. If you don’t possess one, I provide five tasks per goal at no cost.

Following this, the agent will commence processing.

The interactions will be divided into separate tasks.

Upon completing the five tasks, AutoGPT will cease operation. To run more than five tasks, you must input your OpenAI Key.

To deploy it yourself, click on this link and adhere to the provided guidelines.

Conclusion

AutoGPT is a game changer in the field of artificial intelligence and autonomous agents. Its dynamic learning, enhanced context awareness, and customization capabilities make it a powerful tool poised to revolutionize industries and applications. As AutoGPT continues to evolve and integrate with other advanced conversational AI models, the potential for AI to enhance and streamline various aspects of our lives grows exponentially. The future of AI is undoubtedly bright, with AutoGPT leading the way.

Additionally, I’ve developed a section on my blog dedicated to my Generative AI projects. You can view the screenshot below.

picture capture from my blog

That’s it for today!

Follow below some interesting articles talking about AutoGPT:

https://www.linkedin.com/pulse/what-auto-gpt-next-level-ai-tool-surpassing-chatgpt-bernard-marr/

https://en.wikipedia.org/wiki/Auto-GPT

https://levelup.gitconnected.com/autogpt-is-taking-over-the-internet-here-are-the-incredible-use-cases-that-will-blow-your-mind-ac31ea94e06e

THE RISE OF AUTONOMOUS AGENTS: PREPARING FOR THE AI REVOLUTION

How to build an AI chatbot with personalized customer data using Open AI API and GPT Index

By harnessing the potential of AI-powered chatbots and personalized interactions, businesses can revolutionize their customer experience, fostering stronger relationships and driving customer loyalty. Implementing cutting-edge technologies like OpenAI API enables the creation of intelligent chatbots capable of understanding and adapting to individual customer needs, preferences, and concerns. As a result, customers receive tailored support, enjoy seamless interactions, and feel valued, significantly enhancing their overall experience. By prioritizing customer-centric innovation, businesses can position themselves as industry leaders and create lasting, positive impacts on customer satisfaction and brand reputation.

What are OpenAI API and GPT Index?

OpenAI API is a powerful tool provided by OpenAI, a leading AI research organization, that enables developers to access state-of-the-art AI models like GPT-4. GPT, which stands for Generative Pre-trained Transformer, is a groundbreaking language model that has revolutionized natural language processing (NLP) with its ability to generate human-like text.

The GPT Index is a catalog of fine-tuned models, allowing developers to pick and choose the most suitable model for their specific needs. By leveraging OpenAI API and the GPT Index, you can create AI chatbots that provide personalized customer experiences, enhancing user engagement and satisfaction.

How to Fine-tune with OpenAI API?

Fine-tuning is training a pre-trained AI model on a custom dataset to make it more suitable for a specific task or application. In the context of chatbots, this can involve training the model on customer interactions, preferences, and other relevant data. Here’s a step-by-step guide to fine-tuning the OpenAI API:

Gather your data: Collect customer data, including conversation transcripts, customer preferences, and other relevant information. Ensure that your data is cleaned and formatted for training.

Prepare your dataset: Split your data into training and validation sets. The training set will be used to fine-tune the model, while the validation set will help you evaluate the model’s performance.

Select a base model: Choose a suitable base model from the GPT Index, considering factors like size, performance, and language capabilities. The base model will serve as the foundation for your custom chatbot.

Fine-tune the model: Use the OpenAI API to fine-tune your selected model on your dataset. This involves uploading your dataset, specifying the base model, and setting the training parameters. The API will then train the model on your data, adjusting its weights and biases to understand better and generate personalized customer interactions.

Evaluate and iterate: Once the fine-tuning is complete, evaluate the model’s performance on the validation set. Adjust the training parameters and fine-tune the model to improve its performance if necessary.

Integrate the chatbot: After achieving satisfactory performance, integrate your fine-tuned AI chatbot into your desired platform, such as a website or a mobile app, and start providing personalized customer experiences.

Is it possible to fine-tune the new GPT-4 model?

The OpenAI GPT-4 model is anticipated to have the capability for task or domain-specific fine-tuning, akin to its predecessor, GPT-3. However, OpenAI has not yet disclosed the particulars of fine-tuning GPT-4. As the model undergoes further exploration and experimentation by researchers and developers, it is expected that more details about fine-tuning GPT-4 will be revealed in the future.

Let’s dive into my actual fine-tuning experiment.

I created an example training the Open AI API with two articles from my blog about ChatGPT, titled “How can you earn money with ChatGPT and Power BI?” and “Open AI released the new ChatGPT API” this week.” I used this site tool to convert my post contents into text and stored it on my GitHub page. You can add as many files as needed to train your model or easily convert this process to store into a database. If you want to open directly in Google Colab, click this link.

Python
#This is ro run in the Google Colab notebook and has all the code you need to create your own chatbot with custom knowledge base using GPT-3. 

#Follow the instructions for each steps and then run the code sample. In order to run the code, you need to press "play" button near each code sample.

#Download the data for your custom knowledge base
#For the demonstration purposes we are going to use ----- as our knowledge base. You can download them to your local folder from the github repository by running the code below.
#Alternatively, you can put your own custom data into the local folder.

! git clone https://github.com/LawrenceTeixeira/data_to_train.git

# Install the dependicies
#Run the code below to install the depencies we need for our functions


!pip install llama-index
!pip install langchain

# Define the functions
#The following code defines the functions we need to construct the index and query it


from llama_index import SimpleDirectoryReader, GPTListIndex, readers, GPTSimpleVectorIndex, LLMPredictor, PromptHelper, ServiceContext
from langchain import OpenAI
import sys
import os
from IPython.display import Markdown, display

def construct_index(directory_path):
    # set maximum input size
    max_input_size = 4096
    # set number of output tokens
    num_outputs = 2000
    # set maximum chunk overlap
    max_chunk_overlap = 20
    # set chunk size limit
    chunk_size_limit = 600 

    # define prompt helper
    prompt_helper = PromptHelper(max_input_size, num_outputs, max_chunk_overlap, chunk_size_limit=chunk_size_limit)

    # define LLM
    llm_predictor = LLMPredictor(llm=OpenAI(temperature=0.5, model_name="text-davinci-003", max_tokens=num_outputs))
 
    documents = SimpleDirectoryReader(directory_path).load_data()
    
    service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor, prompt_helper=prompt_helper)
    index = GPTSimpleVectorIndex.from_documents(documents, service_context=service_context)

    index.save_to_disk('index.json')

    return index

def ask_ai():
    index = GPTSimpleVectorIndex.load_from_disk('index.json')
    while True: 
        query = input("What do you want to ask? ")
        response = index.query(query)
        display(Markdown(f"Response: <b>{response.response}</b>"))

# Set OpenAI API Key
#You need an OPENAI API key to be able to run this code.

If you don't have one yet, get it by [signing up](https://platform.openai.com/overview). Then click your account icon on the top right of the screen and select "View API Keys". Create an API key.

#Then run the code below and paste your API key into the text input.


os.environ["OPENAI_API_KEY"] = input("Paste your OpenAI key here and hit enter:")

#Construct an index
#Now we are ready to construct the index. This will take every file in the folder 'data', split it into chunks, and embed it with OpenAI's embeddings API.

#*Notice:** running this code will cost you credits on your OpenAPI account ($0.02 for every 1,000 tokens). If you've just set up your account, the free credits that you have should be more than enough for this experiment.


construct_index("/content/data_to_train")

#Ask questions
#It's time to have fun and test our AI. Run the function that queries GPT and type your question into the input. 

#If you've used the provided example data for your custom knowledge base, here are a few questions that you can ask:
#1. What is the new API?
#2. ChatGPT is designed to handle what?
#3. What is BotGPT?
#4. Tell me what are the benefits of BotGPT?
#5. What the relations with Power BI and ChatGPT?
#6. How to make money with chatGPT and Power BI?

ask_ai()

Build AI chatbot with custom knowledge base using Open AI API and GPT index

Conclusion

Developing an AI chatbot that leverages personalized client information through the OpenAI API and GPT Index has the potential to transform customer experiences by providing customized interactions, education, and assistance. This solution offers numerous business prospects, including training models to comprehend a company’s areas of expertise, processes, policies, and protocols. By fine-tuning a model from the GPT Index with the OpenAI API, you can harness the power of AI to enhance user engagement and satisfaction, setting your business apart from the competition.

Embrace the world of AI chatbots and start providing a truly personalized experience for your customers today.

That’s it for today!

Here are the sources I used to create this article:

Open AI – Oficial Fine-tuning documentation

https://www.lennysnewsletter.com/p/i-built-a-lenny-chatbot-using-gpt

https://medium.datadriveninvestor.com/fine-tuning-gpt-3-for-helpdesk-automation-a-step-by-step-guide-516394df7f1

How can you earn money with ChatGPT and Power BI?

In today’s digital age, data is more valuable than ever. Businesses of all sizes are constantly looking for ways to make sense of the vast amounts of data they collect, and that’s where ChatGPT and Power BI come in. These powerful tools can help businesses make data-driven decisions, improve their operations, and ultimately increase their bottom line. If you’re skilled in using these tools, you may be wondering how you can turn that skill into a profit. In this blog post, we’ll explore the different ways you can earn money by using ChatGPT and Power BI. Whether you’re a freelancer, a consultant, or an entrepreneur, there are many opportunities out there for those who know how to use these tools effectively. So, let’s dive in and see how you can monetize your knowledge and skills in ChatGPT and Power BI!

What is chatGPT?

ChatGPT, or Generative Pre-trained Transformer, is a state-of-the-art language generation model developed by OpenAI that has the ability to generate human-like text. It is capable of completing tasks such as writing articles, generating code, and even composing poetry.

How can chatGPT be used to create content?

One of the ways that ChatGPT can be used is to create content for businesses and individuals. By providing ChatGPT with a prompt, it can generate high-quality, unique content that can be used for blogs, social media, and other marketing materials.

How to make money with chatGPT and Power BI?

Power BI is a data visualization tool that allows businesses to analyze and communicate data in an interactive and visually appealing way. Combining the use of ChatGPT and Power BI can help businesses to create engaging and informative content that can lead to increased revenue and improved efficiency. Here are three real examples of how businesses are using ChatGPT and Power BI to increase revenue:

  1. A financial services company is using ChatGPT to generate financial reports and Power BI to visualize the data. By using this combination, the company can create informative and visually appealing reports that help clients to understand their financial information and make better investment decisions.
  2. A marketing agency is using ChatGPT to generate social media posts, and Power BI to analyze the data on engagement, reach, and conversion. By using this combination, the agency can create effective and engaging social media campaigns that help to increase revenue for their clients.
  3. A consulting firm is using ChatGPT to generate client reports and Power BI to visualize the data. By using this combination, the firm can create informative and visually appealing reports that help clients to understand their business information and make better decisions.

Why most people will not succeed?

While the potential for making money with ChatGPT and Power BI is great, most people will not succeed in doing so. This is because it requires a deep understanding of data analysis and the ability to communicate insights effectively to others. Additionally, it requires a significant investment of time and resources to develop the necessary skills and tools to succeed.

Importance of human creativity and putting to work

The importance of human creativity and input cannot be overstated when it comes to using ChatGPT and Power BI. While technology can automate certain tasks, it is not a replacement for human creativity and critical thinking. To truly succeed, businesses must combine the power of technology with the creativity and insight of their human employees.

Conclusion

ChatGPT and Power BI can be powerful tools for businesses looking to increase revenue and improve efficiency. However, it requires a deep understanding of data analysis and the ability to communicate insights effectively to others. Additionally, it requires a significant investment of time and resources to develop the necessary skills and tools to succeed. The importance of human creativity and input cannot be overstated when it comes to using ChatGPT and Power BI. To truly succeed, businesses must combine the power of technology with the creativity and insight of their human employees.

Impressive what we can do with ChatGPT, this post was entirely created by ChatGPT, using the prompt below.

this picture was extracted from ChatGPT
The introduction is also created with ChatGPT

This is just the beginning, ChatGPT is based in GPT-3 and I can already imagine how far we will go once GPT-4 is released.

GPT-4 is a hypothetical model that refers to the next iteration of the GPT series, following GPT-3. The GPT series are large language models that are trained on massive amounts of text data and have the ability to generate human-like text, complete a wide range of language tasks, and even compose poetry. While there is no official release of GPT-4 yet, OpenAI has been actively researching and developing new models in the GPT series, so it is possible that a GPT-4 model will be released in this year.

Some potential improvements that could be made in GPT-4 include:

  • Increased model size: GPT-4 could have even more parameters than GPT-3, which would allow it to have an even greater capacity for understanding and generating text.
  • Improved training data: GPT-4 could be trained on even more diverse and extensive text data, which would allow it to have an even greater understanding of language and a wider range of knowledge.
  • Advanced capabilities: GPT-4 could have even more advanced capabilities than GPT-3, such as the ability to perform more complex language tasks, like writing a book or composing poetry.
  • Improved performance: GPT-4 could have even more accurate and natural language generation than GPT-3, making it even more powerful for various applications.

Finally, ChatGPT is a powerful language generation model that can be used for a wide range of natural language processing (NLP) tasks. From text generation to question answering, language translation, chatbot development, text completion and sentiment analysis, ChatGPT can help businesses and organizations make sense of their data and improve their operations.

One of the key advantages of ChatGPT is its ability to generate human-like text. This can be incredibly valuable for businesses that need to produce large amounts of high-quality content, such as articles, stories, and blog posts, in a short amount of time. Additionally, its ability to answer a wide range of questions can be useful for businesses that want to provide quick and accurate customer service.

Another advantage of ChatGPT is its ability to translate text and perform text summarization, this feature can be used by businesses that operate in multiple languages, or work with international partners.

ChatGPT can also be used to develop chatbots that can engage in natural language conversations with users. This can be incredibly valuable for businesses that want to improve their customer service or provide 24/7 support.

In short, ChatGPT is a versatile and powerful tool that can be used for a wide variety of NLP tasks. Businesses and organizations in many different industries can benefit from its ability to generate human-like text, answer questions, translate text, develop chatbots and perform sentiment analysis. So, if you’re looking to make sense of your data, improve your operations, or simply save time and effort, ChatGPT is definitely worth considering.

If you want to use ChatGPT yourself click here.

That’s it for today!