DAIR.AI 的 Elvis Saravia 呼吁 AI 从业者学习构建 evals,认为不掌握如何正确衡量想改进的对象就谈不上 RSI。他指出 AI 目前擅长的多是可验证问题,开放式场景需要完全不同的改进与衡量方法,应回归硬核科学与评测,而非短期 benchmaxxing。他称该领域机会巨大,团队正在做相关研究与评测,并寻求合作。
Learn to build evals, folks!
You are not RSI-pilled enough if you don't know how to properly measure the thing you want to improve.
It doesn't matter if it's a model, complex codebase, or a business function. This is extremely hard in the real world.
You ever wonder why most of what AI is great at today is around verifiable problems?
The open-ended world requires a completely different approach to improving and measuring models.
We need to go back to hardcore science and evals. Enough of this short-term benchmaxxing.
This space offers so much opportunity. We are doing research and building evals in this space @dair_ai. I am currently chatting with folks building in this space too. Feel free to reach out for potential collab opportunities or just share perspectives.
来源:elvis · x.com