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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?
Amanda Blacklock is president of the Selkirk Musical Theatre Group，详情可参考服务器推荐
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The main rule for data access is max(CPL, RPL) ≤ DPL. For code transfers, the rules get considerably more complex -- conforming segments, call gates, and interrupt gates each have different privilege and state validation logic. If all these checks were done in microcode, each segment load would need a cascade of conditional branches: is it a code or data segment? Is the segment present? Is it conforming? Is the RPL valid? Is the DPL valid? This would greatly bloat the microcode ROM and add cycles to every protected-mode operation.
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