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?
“These platforms were developed for adults. They were developed for adults, but kids are on them. It was never purposeful, like, what’s the product for kids? It was an afterthought, which then means we’re trying to plug holes,” Debra Boeldt, a generative AI psychologist at the family online safety company Aura, told Fortune. “A lot of these companies right now are trying to help, but don’t have the resources to put towards it, or the evidence-based, trained individuals to think about it and plan for it.”
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