AIはきらめく能力の銀河のように見えるが、その中心には肉眼では見えず、すべての構造を結びつける想像を絶するほど巨大なデータのブラックホールが存在している。この比喩は、現代のAIシステムが膨大なデータに依存していることを浮き彫りにしている。
Dwarkesh Patel argues that AI progress is hitting a "data black hole" where the most valuable training data (from expert domains, rare events, and physical interactions) is exponentially harder to obtain, potentially limiting the continued scaling of AI models even as compute becomes cheaper.
This video explores the issue of data transparency in AI, highlighting how major AI companies often withhold details about the datasets used to train their models. It examines the consequences of this lack of transparency, including potential biases, legal challenges, and the difficulty of auditing AI systems for accountability.