How is generative AI being used to create synthetic data in various industries, and what are the most promising applications you’ve observed or foreseen?
In what scenarios might synthetic data fall short compared to real-world data, and how does synthetic data complement real-world data in enhancing the robustness of AD algorithms?
What are the pros and cons of using multiple single-task AI models versus a single foundation model for various tasks?
How are advancements in AI, ML, and computer graphics contributing to more effective world simulation?
From proof-of-concepts to production, how to deploy generative AI applications at scale?
AI, Machine Learning, Reinforcement Learning, Deep Learning
Data Management, Fusion, Labelling, Annotation, Storage
PE
Workshop
Speaker
Lily Xianling Zhang
Technical Project Lead, Data & ML, Ford Motor Company
Technical Lead with 6 years of industry experience in developing state-of-the-art(SOTA) computer vision and deep learning algorithms to production.
We don't just make history -- we make the future. Ford put the world on wheels over a century ago, and our teams are re-inventing icons and creating groundbreaking connected and electric vehicles for the next century. We believe in serving our customers, our communities, and the world. If you do, too, come move the world and make the future with us. Ford is a global company with shared ideals and a deep sense of family. From our earliest days as a pioneer of modern transportation, we have sought to make the world a better place – one that benefits lives, communities and the planet. We are here to provide the means for every person to move and pursue their dreams, serving as a bridge between personal freedom and the future of mobility. In that pursuit, our 186,000 employees around the world help to set the pace of innovation every day.