Part 6/10:
Revisiting the phenomenon of data efficiency, researchers are now exploring scaling laws that could refine the way models learn from training data. This entails moving away from reliance on vast redundant datasets and instead prioritizing unique, informative data points that enhance learning in a more sustainable manner.
In tandem, energy efficiency remains a significant hurdle for AI. Traditional digital computation hinges on fast, reliable bit flipping—an energy-intensive process. By drawing inspiration from biological systems, which often achieve efficiency by operating under less stringent demands, AI could drastically reduce its energy requirements, fostering growth and sustainability.