Meet our Speakers
(partial list – new speakers added weekly)
Sept 29-30 2022
Two days – Three tracks per day
Learn how to put data-centric AI into practice in the real world. Hear from some of the top practitioners in the world today along with some of the most advanced platforms. Explore talks from the leaders of massive data sets used to train some of the largest foundational models on the planet.
Deep learning pioneer, Andrew Ng, coined the term Data-Centric AI:
“The discipline of systematically engineering the data needed to build a successful AI system.“
(AI Generated Image by Stable Diffusion)
In other words, Data-Centric AI focuses on updating the data to solve a problem versus changing the algorithm or code. That’s a complete reversal of how we’ve thought about AI up until now.
Over the last decade, researchers focused on code and algorithms first and foremost. They’d import the data once and generally leave it fixed. If there were problems with noisy data or bad labels they’d usually work to overcome them in the code. Data-Centric AI flips that on its head and says we should fix the data itself. Clean up the noise. Augment the dataset to deal with it. Re-label, so it’s more consistent.
The simple chart here neatly illustrates the difference between model-centric and data-centric AI.
Data-Centric AI is Powering the Next Gen of AI Models