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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过去几年，AI模型规模增长得极其迅猛。2018年训练大型模型通常只需要几百块GPU，2021年一些大型系统已经使用数千块GPU。到了2024年前后，许多生成式AI模型训练集群的规模达到几万块GPU。未来几年，大型AI计算中心很可能会部署数十万GPU。，详情可参考搜狗输入法2026
他表示，定价的核心在于三件事，增长有无可复制的路径，利润和现金流是否足够干净，竞争格局是否足够稳定。“能把这三件事讲清楚的消费公司，在港股现在的定价框架里会更接近成长股，而不是传统消费股。”，推荐阅读体育直播获取更多信息