I Tested ChatGPT ADA for a Data Cleaning Task. It’s Super Helpful but Fails Logical Reasoning

I Tested ChatGPT ADA for a Data Cleaning Task. It’s Super Helpful but Fails Logical Reasoning.

Overview

Author(s): Soner Yıldırım

Originally published on Towards AI.

Let’s see how good and bad it can be
(image created by the author with Midjourney)

A big part of most data-related jobs is cleaning the data. There is usually no standard way of cleaning data, as it can come in numerous different ways.

We encounter inconsistencies, data entry errors, and many other types of issues that need to be handled before the data can be used for downstream processes.

I tested the ChatGPT Advanced Data Analysis (ADA) plugin for a data cleaning task involving a car dataset.

TL;DR ChatGPT ADA is super helpful at using data cleaning libraries to do required tasks but fails to figure out what to do in some… Read the full blog for free on Medium.

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Published via Towards AI

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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.