POWERING HR WITH AI: An In-Depth Guide to ChatGPT for Human Resources Professionals BY Matt Dunn © Copyright 2024 - All rights reserved. Second Edition It is not legal to reproduce, duplicate immense power of ChatGPT as an analytical tool on unstructured data. 5. Interviewing: Conduct in-depth interviews with shortlisted candidates, either in person or via video calls. This stage may involve information . Expanded Word Limit: Capable of handling over 25,000 words, facilitating the creation of in-depth HR reports and analyses . Bing Search and Plugins: Offers real-time information access, crucial for

list manageable. The meeting’s focus on evaluation methodology provided a specialized forum for in-depth discussion, distin- guishing it from broader AI security topics covered in other venues. The four methods (e.g., red teaming, multiple choice benchmarking) by key attributes (e.g., repeatability, depth, generalizabil- ity) to stimulate discussion. Table A.1 (see the appen- dix) presents my interpretation Methodological Framework Discussed in Track 3 Session Methods Judge Repeatability Breadth Speed Depth Generalizability Cost Single output Multiple choice questions Human ✓ ✓ ~ X X ~ Automated ✓ ✓

processing 1:N servers Support Community Community Community + Commercial Analytic Breadth & Depth 10000+ innovative analytic packages 10000+ innovative analytic packages 10000+ innovative packages

performance Improve monetary and non-monetary recognition of staff contributions Improve breadth, depth, quality and timeliness of perform- ance information Improve effectiveness of organizational structures emphasis on quality management and benchmarking Improve process innovation skills of staff Improve depth and breadth of staff technical skills Improve quality and speed of communication with partners Improve management processes Improve business case development and analysis processes Improve breadth, depth and quality of financial information (asset, budget, price/cost, treasury, debtor/creditor, performance

diagnostic tools, such as thermal imaging and real-time energy monitoring, enhances the accuracy and depth of the audit. These tools help uncover hidden inefficiencies and establish a detailed baseline for successful energy audit. Depending on the facility’s needs, energy assessments can vary in scope and depth, ranging from quick assessments that identify major opportunities, to detailed assessments of specific

Chinese company to produce vehicles in China—which was perhaps the most 29 Commission opens first in-depth investigation under the Foreign Subsidies Regulation, European Commission, 16 th February 2024, viewed Atlantic Council, 25 th June 2024, viewed 27 th January 2025, depth- research-reports/issue-brief/a-global-strategy-to-secure-uas-supply-chains/> 108 Pusztaszeri, A, This was the first time that the Commission launched a FSR investigation: Commission opens first in-depth investigation under the Foreign Subsidies Regulation, European Commission, 16 th February 2024, viewed

application of AI technology in the entire process are elaborated in depth. Secondly, the application of AI technology in the manufacturing industry

paper presents development concepts and transformation insights. Furthermore, it conducts an in-depth analy‐ sis and outlook on the trends and key technological directions of the public transit system

性价比，中等价位市场收窄。 4 目 录 CONTENTS 01 中美市场对比分析 02 中国市场深入探究 03 中国用户需求洞察 Comparative analysis In-depth study of Chinese market Insights into Chinese user needs 04 中国企业标杆案例 Chinese outstanding enterprise 教育软硬件厂商 传统教育服务厂商 专注早教领域厂商 (百词斩) 以需求端（提分、考学）为主要驱动因素，通过技术能力开发新品或完善旧品以满足被验证的用户需求 16 中国市场深入探究 In-depth Study of Chinese Market 02 17 ©2024.8 iResearch Inc.

参数调优、特征工程以及模型集成等：  超参数调优：通过网格搜索（Grid Search）或随机搜索 （Random Search）等方法，优化模型的超参数。例如，对 于随机森林模型，可以调整树的深度（max_depth）、最小 样本分割数（min_samples_split）等参数。  特征工程：对原始教学数据进行预处理和特征提取。例如，将 学生的课堂表现数据转换为标准化评分，或将教师的反馈文本 进行情感分析并转化为数值特征。 Stacking）进一步提升预测精度。例如，采用随机森林和 XGBoost 进行集成，可以有效降低过拟合风险并提高泛化能 力。 以下是一个简单的超参数调优示例表： 模型 超参数 取值范围 最优值 随机森林 max_depth [5, 10, 15] 10 随机森林 min_samples_spli t [2, 5, 10] 5 DNN 学习率 [0.001, 0.01, 0.1] 0.01 DNN 隐藏层数