Agenda

In our panel session, we’ll dissect the complexities inherent to responsibly leveraging Generative AI in the midst of an escalating ML arms race. We’ll probe into the ethical implications of large-scale AI experiments and the ongoing parameter wars, weighing the computational demand against potential fallout. As AI regulation efforts globally accelerate, we’ll discuss their influence on deep learning trajectories and the necessary proactive engagement from organizations. In anticipation of a rise in AI incidents due to rapid ML model scaling and hardware complexity, we’ll explore risk mitigation strategies. Lastly, we’ll delve into the increasing significance of data-centric ML systems, focusing on high-quality data acquisition and the role of data quality in minimizing risks associated with swift AI advancements. Join us as we navigate the technical intricacies of AI innovation and responsibility.