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?
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这是干事创业的行动准则：“谋划和推动本地区本部门工作要以贯彻党中央决策部署为前提，创造性开展工作，做到既为一域增光、又为全局添彩。”
L3因为是人车共驾，责任划分、保险赔付等方方面面都需要考虑到。而L4不存在这个问题，出了事就是厂家负责。因为责任明晰，所以落地争议也较少，这就是现状。但是只要法律层面解决L3的责任划分问题，大规模普及也并不是遥遥无期。