Most current AI models are trained in limited locations and then remain static, failing to adapt to user needs or learn from their work. The author advocates for AI systems that are as diverse and distributed as people, incorporating human will and judgment to guide their development. AI
IMPACT Suggests a shift towards more adaptive and user-informed AI development, moving away from static, centrally-trained models.
RANK_REASON The item is an opinion piece discussing the limitations of current AI training methods and proposing an alternative approach.
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