AI Progress Should Be Measured by Capability-Per-Resource, Not Scale Alone: A Framework for Gradient-Guided Resource Allocation in LLMs
PositiveArtificial Intelligence
A new position paper argues for a shift in AI research from focusing solely on scaling model size to measuring capability-per-resource. This approach addresses the environmental impacts and resource inequality caused by the current trend of unbounded growth in AI models. By proposing a theoretical framework for gradient-guided resource allocation, the authors aim to promote a more sustainable and equitable development of large language models (LLMs), which is crucial for the future of AI.
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