This article examines six production trade‑offs that AI engineers face—build vs. buy LLMs, model complexity vs. maintainability, data quantity vs. quality, batch vs. real‑time inference, prompt engineering vs. fine‑tuning, and automation vs. human‑in‑the‑loop—backed by surveys, research studies, and concrete cost analyses.
The article explains how mathematical models bridge science and reality, emphasizing that effective models require a blend of domain expertise, solid methodology, and clear mathematical language, while warning against over‑reliance on black‑box AI and unnecessary complexity.
The article reflects on recent high‑school math modeling contests, questioning why students often overstate their achievements, especially regarding AI‑like models, and proposes a framework of model complexity and cognitive depth to guide more authentic skill development.
In his JD AI Innovation Summit presentation, Prof. Zhou Zhihua examined why deep neural networks have succeeded, identified three essential conditions—layer‑wise processing, internal feature transformation, and sufficient model complexity—highlighted their limitations, introduced the gcforest/deep forest alternative, and emphasized the need for large data, powerful hardware, training tricks, and talent to advance AI research and education.
This article presents a curated collection of insightful machine‑learning diagrams that illustrate key concepts such as test versus training error, under‑ and over‑fitting, Occam’s razor, feature interactions, irrelevant features, basis functions, discriminative versus generative models, loss functions, least‑squares geometry, and sparsity.
