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?
亿邦动力拥有行业权威媒体、智库和马蹄社，长期研究、传播和链接新竞争力品牌。
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在能力的提升、生态的健全、资源的投入影响下，各行各业正在尝试把智能体真正的用起来。根据麦肯锡2025年全球调研显示，约62%的受访组织已在部分业务中尝试智能体（23%为至少一个场景的规模化部署，39%为试验性应用）；但从业务职能的具体采用数据来看，产业对智能体的应用还处于早期阶段：根据该调查，对于智能体应用最多的职能依次是IT、知识管理、营销和服务，以应用最多的IT为例，仅有2%和8%的受访企业IT部门全面规模化（Fully Scaled）和规模化（Scaling）的应用智能体，以及6%和7%的企业IT部门试点(Piloting)和试验(Experimenting)的应用。