Advancing AI in the face of data scarcity

Has AI reached a performance plateau?Larger, newer models seem to be yielding diminishing returns, with only marginal improvements over their forerunners despite ever increasing compute power and storage capacity.As the ‘cumulative sum of human knowledge’ appears to have been exhausted in AI training, could this plateau actually be an unsurmountable wall?In this short note, we explore the challenges of advancing AI in the face of data scarcity, and in particular, we delve into the world of synthetic data generation.  The concept is not new, but where limited and biased statistical methods used to constitute the primary approach, deep learning frameworks are now able to address the weaknesses of their predecessors, enabling the creation of highly realistic and diverse datasets for the purpose of training next-gen AI systems.Can these solutions really put AI back on track for a more sustained progress trajectory?