Петербург приблизился к новому метеорекорду

· · 来源:chart资讯

在「快速達到流利程度」的承諾之外,各種新方法與科技已經徹底改變我們在時間越來越匱乏的時代如何學習語言。其中之一是「微學習」(microlearning)——這種方法將新資訊拆分成小小片段,讓人們能在極短時間內吸收,有時甚至短至幾分鐘或幾秒鐘。它建基於一項稱為「遺忘曲線」的概念:當人們一次接收大量資訊時,隨著時間推移記住的內容反而會更少。

Many people reading this will call bullshit on the performance improvement metrics, and honestly, fair. I too thought the agents would stumble in hilarious ways trying, but they did not. To demonstrate that I am not bullshitting, I also decided to release a more simple Rust-with-Python-bindings project today: nndex, an in-memory vector “store” that is designed to retrieve the exact nearest neighbors as fast as possible (and has fast approximate NN too), and is now available open-sourced on GitHub. This leverages the dot product which is one of the simplest matrix ops and is therefore heavily optimized by existing libraries such as Python’s numpy…and yet after a few optimization passes, it tied numpy even though numpy leverages BLAS libraries for maximum mathematical performance. Naturally, I instructed Opus to also add support for BLAS with more optimization passes and it now is 1-5x numpy’s speed in the single-query case and much faster with batch prediction. 3 It’s so fast that even though I also added GPU support for testing, it’s mostly ineffective below 100k rows due to the GPU dispatch overhead being greater than the actual retrieval speed.

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