Explore the Economic Impact of Generative AI with a Chatbot

Explore the Economic Impact of Generative AI with a Chatbot

Table of Contents

  1. Introduction
  2. The Economic Impact of Generative AI
  3. Extracting Text from PDFs
  4. Making API Calls to OpenAI
  5. Designing the Question-Answer System
  6. Implementing the Front-End Interface
  7. testing the Chatbot
  8. Observations and Insights
  9. The Future of AI Consultancy

Introduction In this article, we will explore the fascinating domain of generative AI and its impact on the economy. We will dive into a recent report shared by McKinsey, which highlights the potential of generative AI in driving innovation and creating new products and services. Instead of reading the entire report, we will leverage the power of AI to build a chatbot that can summarize and answer questions about the document. Let's get started!

The Economic Impact of Generative AI Generative AI has revolutionized various industries by enabling machines to generate creative and innovative outputs. According to the McKinsey report, the economic potential of generative AI is estimated to be between $6.1 trillion to $7.9 trillion annually. This staggering figure demonstrates the vast opportunities that generative AI presents for businesses. By harnessing the power of AI, companies can drive growth, develop new strategies, and uncover untapped markets.

Extracting Text from PDFs Before we can analyze the report, we need to extract the text from the PDF document. To accomplish this, we will utilize a Python function called "extract PDF text." This function reads the PDF, extracts the paragraphs page by page, and saves them in a list. By leveraging the PDF reader package, we can easily access and manipulate the text for further processing.

Making API Calls to OpenAI Once we have extracted the text, the next step is to make API calls to OpenAI. We will use the GPT (Generative Pre-trained Transformer) model to generate questions and answers based on the provided text. First, we will create an API call to generate questions using the "get question" function. This function sends the text as a prompt to OpenAI and retrieves a list of questions. By applying this function to our dataset, we can generate a column of questions for each paragraph.

Designing the Question-Answer System With the questions in place, we can now proceed to design the question-answer system. This involves making another API call to OpenAI's completion function, using the "get answer" function. This function prompts OpenAI to generate an answer based on the given context and question. By applying this function to each row of our dataset, we can generate a column of corresponding answers. This system allows us to retrieve specific information from the report using AI-powered chatbot technology.

Implementing the Front-End Interface To provide a user-friendly experience, we need to implement a front-end interface for our chatbot. The interface allows users to select similarity functions, models, and specific domains. In our case, we have implemented a new domain based on the McKinsey generative AI report. Users can interact with the chatbot by selecting the relevant domain and entering their questions. The chatbot will then return the most appropriate answer based on the context and question.

Testing the Chatbot To ensure the accuracy and reliability of our chatbot, we conducted several tests. We compared the generated answers to the information present in the report itself. In most cases, the chatbot provided relevant and sensible answers. However, we discovered an interesting observation. The chatbot managed to retrieve information from other parts of the report, even if the question originated from a different section. This showcases the machine's ability to capture contextual information and generate accurate responses.

Observations and Insights Our exploration of generative AI and its application in the field of consultancy opens up exciting possibilities. With the advancements in AI technology, we can envision a future where AI consultants guide companies in developing effective corporate strategies and driving revenues. The chatbot backed by the McKinsey generative AI report is just the beginning. As AI continues to evolve, we can expect even more sophisticated and intelligent systems to provide valuable insights and recommendations.

The Future of AI Consultancy The integration of generative AI into the world of consultancy holds immense potential. By leveraging AI algorithms, companies can access expert advice and analysis at a fraction of the cost. AI consultants can provide personalized recommendations, untangle complex business problems, and assist in strategic decision-making. As AI technology progresses, the boundaries of what is possible in AI consultancy will continue to expand, ultimately shaping the future of businesses worldwide.

Highlights

  • Generative AI has the potential to drive innovation and create new products and services.
  • The economic impact of generative AI is estimated to be between $6.1 trillion to $7.9 trillion annually.
  • Extracting text from PDFs is the first step in analyzing and processing document data.
  • API calls to OpenAI allow us to generate questions and answers based on the provided text.
  • The question-answer system enables the chatbot to retrieve specific information from the report.
  • The front-end interface provides a user-friendly experience for interacting with the chatbot.
  • Testing the chatbot revealed its ability to capture contextual information and generate accurate responses.
  • AI consultancy holds the potential to revolutionize corporate strategy and decision-making.
  • The future of AI consultancy is bright, with AI algorithms providing personalized insights and recommendations.

FAQ

Q: How accurate is the chatbot in generating answers? A: The chatbot's accuracy is high, with most answers being relevant and sensible. However, occasional inconsistencies may occur due to the complexity of the text and the limitations of the AI model.

Q: Can the chatbot retrieve information from different sections of the report? A: Yes, the chatbot can retrieve information from different parts of the document based on contextual understanding. It leverages the similarity ranking algorithm to locate relevant answers.

Q: What other applications can generative AI have? A: Generative AI can be applied in various industries, including content creation, creative design, language translation, and data analysis. Its potential is vast and continually expanding.

Q: How can AI consultancy benefit businesses? A: AI consultancy offers cost-effective access to expert advice and analysis, enabling companies to make informed decisions and drive growth. It provides personalized recommendations and insightful guidance.

Q: What is the future of AI consultancy? A: The future of AI consultancy is promising. As AI technology advances, we can expect AI consultants to become more intelligent and capable of assisting businesses in strategic decision-making and driving revenues.

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