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Why Data Quality Matters in the Age of Generative AI

Dataiku

Generative AI is rapidly transforming the data science landscape. Its ability to create synthetic data promises exciting possibilities for data augmentation and improved model performance.

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The Future of AI: High Quality, Human Powered Data

Smart Data Collective

Artificial Intelligence (AI) has significantly altered how work is done. However, AI even has a bigger impact by enhancing human capabilities. Human labeling and data labeling are however important aspects of the AI function as they help to identify and convert raw data into a more meaningful form for AI and machine learning to learn.

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Data Quality in the Age of AI: A User Story

Dataiku

Today, more than 90% of AI practitioners experience significant cascading data errors , and poor data quality is estimated to cost companies 20% of revenue. The bottom line: bad data is ubiquitous and costly, and it also reduces organizations’ overall AI productivity.

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Six Data Quality Dimensions to Get Your Data AI-Ready

TDAN

If you look at Google Trends, you’ll see that the explosion of searches for generative AI (GenAI) and large language models correlates with the introduction of ChatGPT back in November 2022.

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The Alation State of Data Culture Report - Q1 2021

Companies are expected to spend nearly $23 billion annually on AI by 2024. This report explores AI obstacles, like inherent bias and data quality issues, and posits solutions by building a data culture. What could go wrong?

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Microsoft’s WaveCoder and CodeOcean Revolutionize Instruction Tuning

Analytics Vidhya

This innovative technique aims to generate diverse and high-quality instruction data, addressing challenges associated with duplicate data and limited control over data quality in existing methods.

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To understand the risks posed by AI, follow the money

O'Reilly on Data

Given that our leading scientists and technologists are usually so mistaken about technological evolution, what chance do our policymakers have of effectively regulating the emerging technological risks from artificial intelligence (AI)? However, there is one class of AI risk that is generally knowable in advance.

Risk 221
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LLMOps for Your Data: Best Practices to Ensure Safety, Quality, and Cost

Speaker: Shreya Rajpal, Co-Founder and CEO at Guardrails AI & Travis Addair, Co-Founder and CTO at Predibase

However, productionizing LLMs comes with a unique set of challenges such as model brittleness, total cost of ownership, data governance and privacy, and the need for consistent, accurate outputs.