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?
Photograph: Simon Hill
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AIO requires understanding how language models decide which sources to reference when answering questions. These models don't follow the same rules as search engine algorithms. They're not counting backlinks or analyzing page load speed. They're evaluating whether content provides clear, accurate, comprehensive answers to questions people actually ask. They're assessing credibility through different signals than traditional search engines use. They're making probabilistic decisions about which information best satisfies a query based on patterns learned during training and information retrieved during real-time web searches.
Хинштейн предупредил жителей российского региона, что в соцсетях намеренно распространяют большое количество фейков. Он напомнил, что вся достоверная информация публикуется на официальных ресурсах.
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