Join more than 30 leading AI researchers and practitioners to discuss the shifting paradigm from model centric AI to data centric AI.
During more than 16 keynotes and panel sessions we will discuss the latest AI discoveries that have a true impact on the development of future technologies.
While AI has been pretty focused on models, the real-world experience of those who put models into production is that most of the time the data matters more.
The DCAID brings together academics and practitioners to share their vision and ideas about the future of AI.
Some of the topics covered:
Data quality monitoring and improvement
Model training with noisy data
MLOps at scale
Declarative ML systems
Data-centric AI case studies
And much more.
Overview of France's strategy on data sovereignty
Renaud Vedel, Coordinator of France’s national strategy on Artificial Intelligence
Introduction to Data Centric AI
Francois-Xavier Leduc, CEO and Cofonder of Kili Technology
Changing the world by changing the data
Anna Rogers, Postdoctoral Researcher at the University of Copenhagen
Better models through better training data
Edouard d’Archimbaud, Co-Founder & CTO at Kili Technology
Resources for multilingual NLP in the neural era: the examples of OSCAR and CamemBERT
Benoît Sagot, Research Director at Inria
Panel 1: Data in practice.
How data quality can be monitored and improved?
Thomas Michel, Senior Data Scientist at Jellysmack
Aymen Shabou, Head of AI at Groupe Crédit Agricole
Roland Acra, Senior VP & CTO at Cisco
From cleaning before ML to cleaning for ML
Félix Neutatz, Research Associate at Technische Universität Berlin
Limitations of Autoregressive Models and Their Alternatives
Chu-Cheng Lin, Research Assistant at Carnegie Mellon University
Group fairness: recent developments and challenges.
Evgenii Chzhen, Research scientist at CNRS/Université Paris-Saclay
The role of data in end to end learned autonomy
Fergal Cotter, Head of the Perception team at Wayve
Continual learning and interactive data collection
Antoine Bordes, AI Researcher at Facebook
The role of data-centric AI in visual control
Karim Sayadi, Delivery Manager at OCTO Technology
Panel 2: Model in practice. How can we train models with imperfect labels? How DevOps for data centric AI can help scale AI in the company?
Léo Dreyfus-Schmidt, VP of Research at Dataiku
Anne Wu, PhD Student at Cornell University
Jean-Louis Quéguiner, Research & Innovation at OVHcloud
Victor Storchan, AI/ML Lead at JPMorgan Chase & Co
Declarative ML systems
Piero Molino, Co-Founder at Stealth Startup
Human in the loop: Lessons learned from AI projects in enterprise
Khemon Beh, Managing Data Scientist
Why and how to care about ML ethics
Clement Delangue, Hugging Face CEO and co-founder
Augmenting bladder cancer diagnostic utilizing unstructured data
Thibaut Troude, CTO at VitaDX
Closing remarks and physical event
Opening of the cocktail
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