This site uses cookies to improve your experience. To help us insure we adhere to various privacy regulations, please select your country/region of residence. If you do not select a country, we will assume you are from the United States. Select your Cookie Settings or view our Privacy Policy and Terms of Use.
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Used for the proper function of the website
Used for monitoring website traffic and interactions
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Strictly Necessary: Used for the proper function of the website
Performance/Analytics: Used for monitoring website traffic and interactions
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 dataquality What if we could change the way we think about dataquality?
Much like a solid foundation is essential for a structure's stability, an AI model's effectiveness is fundamentally linked to the quality of the data it is built upon. In recent years, it has become increasingly evident that even the most advanced AI models are only as good as the data they are trained on.
From basic driver assistance to fully autonomous vehicles(AVs) capable of navigating without human intervention, the progression is evident through the SAE Levels of vehicle automation. Despite most scenarios being solvable with traditional methods, unresolved corner cases highlight the necessity for AI-driven solutions.
Researchers from Shanghai AI Laboratory, UNC Chapel Hill, Adobe Research, and Nanjing University proposed the Self-Refining Data Flywheel (SRDF), a system designed to iteratively improve both the dataset and the models through mutual collaboration between an instruction generator and a navigator.
Instead of relying on organic events, we generate this data through computer simulations or generative models. Synthetic data can augment existing datasets, create new datasets, or simulate unique scenarios. Specifically, it solves two key problems: datascarcity and privacy concerns.
Ensuring dataquality, addressing potential biases, and maintaining strict privacy and security standards for sensitive medical data are the major concerns. BloombergGPT, for instance, with its 50-billion parameter size, is fine-tuned on a blend of proprietary financial data, embodying a pinnacle of financial NLP tasks.
Summary: Generative AI is transforming Data Analytics by automating repetitive tasks, enhancing predictive modelling, and generating synthetic data. By leveraging GenAI, businesses can personalize customer experiences and improve dataquality while maintaining privacy and compliance.
We organize all of the trending information in your field so you don't have to. Join 15,000+ users and stay up to date on the latest articles your peers are reading.
You know about us, now we want to get to know you!
Let's personalize your content
Let's get even more personalized
We recognize your account from another site in our network, please click 'Send Email' below to continue with verifying your account and setting a password.
Let's personalize your content