ChatGPT As OCR For PDFs: Your New ETL Tool for Data Analysis

ChatGPT As OCR For PDFs: Your New ETL Tool for Data Analysis.


Author(s): David Leibowitz

Originally published on Towards AI.

Coding in English at the speed of thought
How To Use ChatGPT as your next OCR & ETL Solution, Credit: David Leibowitz

For a recent piece of research, I challenged ChatGPT to outperform Kroger’s marketing department in earning my loyalty. Could a generative AI, when fed my transaction history, create a marketing strategy more compelling than weekly coupons for eggs and produce?

The broader question was whether ChatGPT could advise marketers in creating valuable customer insights and consumer marketing strategies for growth and retention using real-world data for mass personalization. The experiment would use my own purchase receipts to test ChatGPT’s ability to conduct business analysis on a limited data… Read the full blog for free on Medium.

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Published via Towards AI


AI Applications

One AI application for businesses facing the choice between open-source and proprietary models to deploy generative AI is natural language processing (NLP) for customer service or support chatbots. Businesses can utilize generative AI models to develop chatbots that can understand and respond to customer queries in a more human-like manner. The choice between open-source and proprietary models can impact the accuracy, scalability, and customization capabilities of the NLP models deployed in these chatbots.

Additionally, another AI application is the development of recommendation systems. Generative AI models can be used to create personalized recommendations for products or content based on user behavior and preferences. The choice between open-source and proprietary models can affect the quality of the recommendations, as well as the ability to tailor the recommendation system to specific business needs.

Furthermore, businesses can leverage generative AI for content generation, such as automated text summarization, language translation, and creative writing. The choice between open-source and proprietary models can influence the linguistic fluency, coherence, and originality of the generated content.

In each of these applications, the decision between open-source and proprietary models for generative AI deployment can significantly impact the performance, interpretability, and ethical considerations of the AI systems utilized by businesses.