Платон Щукин (Редактор отдела «Экономика»)
Source: Computational Materials Science, Volume 267
，推荐阅读爱思助手下载最新版本获取更多信息
这意味着，邮储银行“刘建军时代”的五年长跑画上了句号。回望这位银行老将的职业生涯，其留下的并非只是几份亮眼的财报，更是一家国有大行在复杂多变的环境中，坚韧生长的深刻足迹。
Professor Michael Wooldridge has given this year’s Royal Society’s Michael Faraday Prize lecture. He speaks to Tom Whipple about why the AI we have is not what he wanted it to be; rational. And science columnist at the Financial Times Anj Ahuja brings her favourite new science to discuss.
As a data scientist, I’ve been frustrated that there haven’t been any impactful new Python data science tools released in the past few years other than polars. Unsurprisingly, research into AI and LLMs has subsumed traditional DS research, where developments such as text embeddings have had extremely valuable gains for typical data science natural language processing tasks. The traditional machine learning algorithms are still valuable, but no one has invented Gradient Boosted Decision Trees 2: Electric Boogaloo. Additionally, as a data scientist in San Francisco I am legally required to use a MacBook, but there haven’t been data science utilities that actually use the GPU in an Apple Silicon MacBook as they don’t support its Metal API; data science tooling is exclusively in CUDA for NVIDIA GPUs. What if agents could now port these algorithms to a) run on Rust with Python bindings for its speed benefits and b) run on GPUs without complex dependencies?