引导生成式人工智能的应用开发

IF 2.8 3区计算机科学 Q2 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

IEEE Micro Pub Date : 2024-08-23 DOI:10.1109/mm.2024.3425209

Shane Greenstein

引用次数: 0

摘要

在最早的原型中，GitHub CoPilot 与其他工具截然不同。它展示了一种非凡的能力，能将中级编码员的工作速度提高 20% 至 40%，主要是在使用 Python 等标准化语言时。在劳动密集型活动中，这样的生产率提升意义重大，而在劳动密集型活动中，这样的突破实属罕见。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Navigating Applications Development in Generative AI

In its earliest prototypes, GitHub CoPilot stood apart from other tools. It demonstrated a remarkable ability to accelerate the work of intermediate coders by 20% to 40%, mainly with standardized languages like Python. This is a significant productivity gain in a labor-intensive activity, where such breakthroughs are rare.

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来源期刊

IEEE Micro 工程技术-计算机：软件工程

CiteScore

7.50

自引率

0.00%

发文量

164

审稿时长

>12 weeks

期刊介绍： IEEE Micro addresses users and designers of microprocessors and microprocessor systems, including managers, engineers, consultants, educators, and students involved with computers and peripherals, components and subassemblies, communications, instrumentation and control equipment, and guidance systems. Contributions should relate to the design, performance, or application of microprocessors and microcomputers. Tutorials, review papers, and discussions are also welcome. Sample topic areas include architecture, communications, data acquisition, control, hardware and software design/implementation, algorithms (including program listings), digital signal processing, microprocessor support hardware, operating systems, computer aided design, languages, application software, and development systems.