Personal benchmarks vs HumanEval - with Nicholas Carlini of DeepMind
Aug 28, 2024 · 1:07:28
Nicholas Carlini, a DeepMind research scientist, argues AI models are practically useful for personal tasks despite flaws, and personal benchmarks tailored to individual use cases matter more than generic leaderboards. He shares from his 'How I Use AI' post: using LLMs for ephemeral software, kickstarting Docker, debugging by pasting error messages. He built a personal benchmark DSL that runs code from real chat history. On security, he explains buying expired domains from LAION-400M allowed poisoning any model trained on it, and he extracted OpenAI's Ada and Babbage model dimensions via API (with permission). He also recovered training data from GPT-3.5 by repeating a word until ChatGPT output verbatim sequences. He prefers attacking over defending because it's more fun and essential for discovering real vulnerabilities.
