But in the worst worst-case scenario, we don’t have any control. Instead, the station will crack through the atmosphere. Sure, many pieces will likely end up in the ocean, but some might hit people, possibly in a town or a city. The station could break apart across thousands of miles and multiple continents. This would be exceedingly hard to anticipate. As NASA puts it, “Calculating the probability of this penetration cascading into loss of deorbit capability has a very large range of variables, making predictions ineffective.”
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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