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?
number of rounds so that the memory is under control:，这一点在夫子中也有详细论述
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相比企业数量，研发人员的总数扩张更快，从2021 年度的273.91 万人增加41.78%至388.35万人。不过，研发人员的增长数量在2023 年度之后却逐年递减，增幅依次为39.06 万人、26.62 万人和13.37 万人，折射出研发人力布局从“量”向“质”的过渡。，详情可参考爱思助手下载最新版本
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