Скачать или смотреть What do AI Benchmarks Actually Mean?! A Fast Breakdown (MMLU, SWE-bench, & More Explained)

Ever see a headline like 'New AI smashes MMLU benchmark' and wonder what that actually means? The truth is, not all AI tests are created equal. In this video, we cut through the marketing hype to provide a professional, technical breakdown of the 10 most important AI benchmarks, explaining not just what they are, but how they work and what makes them different.

This deep dive is for those who want to understand the science, not just the scores.

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In this analysis, we dissect the crucial differences between seemingly similar evaluations, giving you the vocabulary and context to properly interpret AI progress.

We cover:

CODING BENCHMARKS: The critical distinction between LiveCodeBench (testing pure algorithmic skill in a contest setting) and SWE-bench (testing real-world code maintenance in messy GitHub repositories).

MATH REASONING: The difference between the MATH benchmark (measuring broad curriculum knowledge) and elite competitions like AIME & HMMT (measuring deep, creative ingenuity).

AGENCY & CONTROL: How TerminalBench evaluates an AI's practical autonomy to perform tasks, while IFEval isolates its ability to follow complex and negative constraints—a key to safety and reliability.

KNOWLEDGE & REASONING: The unique purpose of ZebraLogic (pure symbolic logic), GPQA (synthesis of expert, "Google-proof" knowledge), and MMLU (massive-multitask retention of academic and professional knowledge).