article thumbnail

Innovations in Analytics: Elevating Data Quality with GenAI

Towards AI

Data analytics has become a key driver of commercial success in recent years. The ability to turn large data sets into actionable insights can mean the difference between a successful campaign and missed opportunities. Flipping the paradigm: Using AI to enhance data quality What if we could change the way we think about data quality?

article thumbnail

EU AI Act: What businesses need to know as regulations go live

AI News

They must demonstrate tangible ROI from AI investments while navigating challenges around data quality and regulatory uncertainty. After all, isnt ensuring strong data governance a core principle that the EU AI Act is built upon? To adapt, companies must prioritise strengthening their approach to data quality.

professionals

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

The Pace of AI: The Next Phase in the Future of Innovation

Unite.AI

Since the emergence of ChatGPT, the world has entered an AI boom cycle. Rethinking AI’s Pace Throughout History Although it feels like the buzz behind AI began when OpenAI launched ChatGPT in 2022, the origin of artificial intelligence and natural language processing (NLPs) dates back decades. Then came ChatGPT.

article thumbnail

Paul O’Sullivan, Salesforce: Transforming work in the GenAI era

AI News

ChatGPT, for example, amassed 100 million users in a mere two months. “If Addressing this gap will require a multi-faceted approach including grappling with issues related to data quality and ensuring that AI systems are built on reliable, unbiased, and representative datasets.

Big Data 341
article thumbnail

This AI Paper Propose AugGPT: A Text Data Augmentation Approach based on ChatGPT

Marktechpost

Recent NLP research has focused on improving few-shot learning (FSL) methods in response to data insufficiency challenges. While these methods enhance model capabilities through architectural designs and pre-trained language models, data quality and quantity limitations persist. This process enhances data diversity.

BERT 126
article thumbnail

The importance of data ingestion and integration for enterprise AI

IBM Journey to AI blog

According to CNN, some companies imposed internal bans on generative AI tools while they seek to better understand the technology and many have also blocked the use of internal ChatGPT. The entire generative AI pipeline hinges on the data pipelines that empower it, making it imperative to take the correct precautions.

article thumbnail

In 2025, GenAI Copilots Will Emerge as the Killer App That Transforms Business and Data Management

Unite.AI

But it means that companies must overcome the challenges experienced so far in GenAII projects, including: Poor data quality: GenAI ends up only being as good as the data it uses, and many companies still dont trust their data.

LLM 111