dailyai.report

A 1991 meeting in Munich laid early groundwork for modern neural network scaling. Researchers discussed the limitations of backpropagation and the necessity of larger datasets long before compute became cheap. This historical retrospective highlights that current breakthroughs rely on decades-old theoretical foundations. Practitioners can trace today's architecture back to these specific academic debates.