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They started out in the NFL as tenants at Wrigley Field, sharing the baseball cathedral with the Cubs for 50 seasons before the league insisted all teams play in a stadium with a capacity of at least 50,000. So in 1971, the Bears decamped to Soldier Field, where they’ve been ever since – save for a season-long “road trip” in 2002 to the University of Illinois’ Memorial Stadium during renovations. Soldier Field is prime football real estate: neoclassical, on the downtown lakefront, with sweeping views of one of America’s most sumptuous skylines. But the lease terms are crazy, the city park district (which owns the stadium) is a borderline slumlord, and the Bears – star-crossed to play in the league’s oldest and smallest stadium while representing its third-largest market – have outgrown the place.
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?。业内人士推荐一键获取谷歌浏览器下载作为进阶阅读