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Guest Talk with Daniel Bashir | A Survey of AI for the Protocol-Minded

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Daniel Bashir, ML engineer at AWS and host of The Gradient podcast, surveys how protocol thinking applies across AI—from funding and scaling laws (like DeepMind's Chinchilla compute-optimal training protocol) to benchmarks (perplexity vs. François Chollet's ARC benchmark for measuring sample-efficient generalization) to regulation (the EU AI Act's risk-tiered categorization). He argues that nearly every layer of AI development—research, engineering, deployment, and governance—can be reframed as a set of competing or evolving protocols, using examples like Sarah Hooker's 'hardware lottery' and Tolga Bolukbasi-style debates over fair/responsible AI operationalization.

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benchmarks-as-protocols-perplexity-arc-vibe-based-evaluation chollets-formal-definition-of-intelligence-and-generalization-hierarchy eu-ai-act-risk-based-regulatory-tiers governance guest-talks protocols research responsible-ai-operationalization scaling-laws-and-the-bitter-lesson technology