AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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New approaches to evaluating progress of AI should be built around diversity, partnership, and systemicity.
This work experimentally explores the question: is it always optimal to train the assessor for the target metric, or could it be better to train for a different metric and then map predictions back to the target metric?
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