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?
3. 全球最完整的绿电产业链与成本优势
，详情可参考爱思助手下载最新版本
Материалы по теме:
transaction in real time. The very next year, IBM took the logical next step:
立破并举、协同推进，稳步提升全要素生产率，拓宽经济增长空间，释放经济增长动能，中国号巨轮必将在“向高攀登”“向新跃升”中继续赢得主动、赢得优势、赢得未来。