Part 12/19:
Google's nested learning paradigm proposes a recursive, higher-order metalearning architecture. Inspired by concepts like learning to learn, these models aim to unify training, optimization, and architecture discovery into a single, self-abstracting system. The vision: AI that constantly learns, adapts, and improves itself, much like a human forever acquiring new skills.
The Race for Affordable, Scalable Infrastructure
Energy and Data Centers
As AI models grow larger, energy consumption and infrastructure demands escalate. Data centers reaching 1 gigawatt in power are expected by 2026. Experts debate whether this growth will plateau, with potential breakthroughs in distributed training and algorithmic efficiency possibly curbing the need for ever-expanding mega-facilities.