But they also point to something we don’t share with the other new pseudo-intelligences emerging today: machine intelligence.
In his letter, Miliband said the government's modelling "accounts for potential emissions from data centres through our projection of overall electricity demand growth, which reflects broader economic trends".
，详情可参考heLLoword翻译官方下载
This creates both an opportunity and a maintenance requirement. The opportunity is that regularly updating content can improve AI citation rates even if the core information hasn't changed dramatically. The requirement is that high-performing content needs periodic refreshes to maintain its competitive position as newer articles on the same topics emerge.
12月24日，北京奥林匹克公园龙形水系，不少市民在湖面上滑冰。本版摄影/新京报记者 王子诚
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?