文章信息: 作者： kaiwu; 日期：2024年12月04日; 点击数：454

https://www.ibm.com/cn-zh/topics/generative-ai

生成式 AI(generative artificial inteligence)，有时也称作 gen AI，是一种人工智能 (AI)，能够创建原创内容（例如文本、图像、视频、音频或软件代码）以响应用户的提示或请求。

1. transform 模型

经典论文

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017). Attention Is All You Need (arXiv:1706.03762). arXiv. https://doi.org/10.48550/arXiv.1706.03762

视频解读

https://www.bilibili.com/video/BV1gG411a7bw/

科技参考解读

https://www.dedao.cn/course/article?id=3bezDG7wBonmJwgeE9JvQkAg5PyO1x

2.国际网站

2.1 chatGPT from openAI

https://chat.openai.com/

2.2 gemini from google

https://gemini.google.com/

2.3 claude from anthropic

https://www.anthropic.com/claude

2.4 llama from meta

https://llama.meta.com/llama3/

3.国内网站

3.1 秘塔metaso

https://metaso.cn/

3.2 月之暗面kimi

https://kimi.moonshot.cn/

3.3 文心一言（baidu.com）

https://yiyan.baidu.com/

3.4 通义千问（阿里云）

https://tongyi.aliyun.com/

3.5 讯飞星火（科大讯飞公司iflytek）

https://xinghuo.xfyun.cn/desk

3.6 元宝AI（腾讯）

https://hunyuan.tencent.com/

3.7 豆包AI（字节跳动）

https://www.doubao.com/chat

3.8 音乐创作suno

https://suno.g-mi.cn/#/

4.具体练习

4.1 检索问题

https://metaso.cn/

检索短语：旅游体验的本真性问题

检索短语：什么是旅游体验中的建构主义本真？请举出5个例子

4.2 检索代码

https://www.doubao.com/chat

检索短语：如何用python导入json数据

检索短语：如何把导入的数据另存为feather格式

4.3 总结文稿

https://www.doubao.com/chat

Wang, N. (1999). Rethinking Authenticity in Tourism Experience. Annals of Tourism Research, 26(2), 349–370. https://doi.org/10.1016/S0160-7383(98)00103-0

英文论文全文下载：

http://kaiwu.city/openfiles/rethinking_authenticity1999wang_ning.pdf

短语：请用200字总结一下这个文件

短语：请列出小标题，从6个方面总结一下这个文件

短语：请从5个方面比较一下建构主义本真（Constructive authenticity）与存在主义本真（Existential authenticity）的区别

短语：请从5个方面，比较一下reality、authenticity、originality这3个词的区别和联系

4.4 OCR识别

https://www.doubao.com/chat

GAI平台：

doubao AI

https://www.doubao.com/chat

或

KIMI

https://kimi.moonshot.cn/

短语：请识别为文字，并用表格整理一下

4.5 翻译

https://www.doubao.com/chat

短语：推迟2天，周六下午两点来我办公室讨论一下，请翻译为英文

短语：请提供8个版本

短语：Happiness through vacationing: Just a temporary boost or long-term benefits，请翻译为中文

短语：Although the potentialities of artificial intelligence (AI) are motivating its fast integration in organizations, our knowledge on how to capture organizational value out of these investments is still scarce. Relying on an approach to dynamic capabilities that focuses on the team level, we examine how humans and AI create interactions that engage both agents in productive dialogue for value co-creation. Our analysis is based on a longitudinal case of the development of a recruitment algorithm at a national subsidiary of Santander bank. Our results allow to identify three main sets of human-AI teaming interactions: achieving interoperability, building trust, and producing mutual knowledge gains. We elaborate a set of propositions on how the value of AI is increased when such interactions are created through productive dialogue, opening the scope for further research on the teaming dynamics that turn the collaboration between both agents into a source of value creation for companie. 请翻译为中文

这段话来自论文的摘要：

Simon, C., Revilla, E., & Jesus Sauenz, M. (2024). Integrating AI in organizations for value creation through Human-AI teaming: A dynamic-capabilities approach. Journal of Business Research, 182, 114783. https://doi.org/10.1016/j.jbusres.2024.114783

4.6 诗歌

https://www.doubao.com/chat

短语：请依据《水调歌头》词牌，做出一首词，关于大学生春天郊游

短语：注意押韵，使用安、然，an这种韵脚

4.7 数据分析

https://www.doubao.com/chat

（1）示例数据集 (tourist satisfaction)

调查问卷

http://kaiwu.city/openfiles/tourist_satisfaction_questionnaire_cn.pdf

No. 变量名变量名标签
1 sid 编号
2 gender 性别
3 byear 出生年份
4 region 区域
5 income 月收入
6 expense 旅游支出
7 type3 旅游目的地类型
8 type2 旅游活动类型
9 thotel 酒店类型
10 sat1 景区sat
11 sat2 住宿sat
12 sat3 餐饮sat
13 sat4 交通sat
14 sat5 娱乐sat
15 sat6 购物sat
16 ri1 评论数量imp
17 ri2 评论的发表日期imp
18 ri3 评论相关性imp
19 ri4 评论中的正向评价imp
20 ri5 评论发布者的资信度imp
21 rp1 评论数量per
22 rp2 评论的发表日期per
23 rp3 评论相关性per
24 rp4 评论中的正向评价per
25 rp5 评论发布者的资信度per
26 te1 刺激感
27 te2 活动偏好
28 te3 旅游经历
29 te4 兴奋感
30 te5 寻求解放
31 te6 自由感
32 te7 新鲜
33 te8 恢复
34 zh1 人多不自在
35 zh2 不喜欢人际交往
36 zh3 不擅长人际交往
37 zh4 避免与人直接交流
38 zh5 外出很累
39 zh6 尽量不外出
40 zh7 外出麻烦
41 latitude 纬度
42 longitude 经度

Value labels gender
1 男
2 女.

Value labels region
1 华中
2 华东
3 华北
4 东北
5 西北
6 西南
7 华西.

Value labels type3
1 自然风光类型
2 历史文化类型
3 自然风光与历史文化混合的类型.

Value labels type2
1 观赏型
2 参与型.

Value labels thotel
1 经济型酒店
2 豪华型酒店
3 民宿
4 酒店式公寓.

（2）通用数据格式 (没有 variable labels变量名标签和 value labels变量值标签)

http://kaiwu.city/openfiles/tourist.csv

短语：请总结一下这个数据集

短语：请提供对性别gender变量进行频数分析的python代码

4.8 TTS (text to speech)

https://elevenlabs.io/

----

https://en.wikipedia.org/wiki/Generative_artificial_intelligence

Generative artificial intelligence (generative AI, GenAI, or GAI) is artificial intelligence capable of generating text, images, videos, or other data using generative models, often in response to prompts. Generative AI models learn the patterns and structure of their input training data and then generate new data that has similar characteristics.

4.9 创作歌曲

https://www.doubao.com/chat

短语：我想创作一首歌曲，用【自定义歌词】，这首歌是【摇滚】音乐风格，传达【兴奋】的情绪，使用【男声】音色。

自定义歌词：再别康桥，徐志摩

轻轻的我走了，

正如我轻轻的来；

我轻轻的招手，

作别西天的云彩。

***

悄悄是别离的笙箫；

夏虫也为我沉默，

沉默是今晚的康桥！

悄悄的我走了，

正如我悄悄的来；

我挥一挥衣袖，

不带走一片云彩。

4.10 生成图片

https://www.doubao.com/chat

短语：请生成一张图片，1个女孩早晨在海边跑步，身着红色T恤，扎马尾辫，海里面有小岛，天上有海鸥，可以看到朝霞。

短语：请生成一张图片，漫画风格，场景是酒店的大堂，展现人工智能对酒店的影响，要有员工、顾客、机器人。

4.11 zotero整合AI

文献管理软件zotero

http://kaiwu.city/index.php/zotero

logo zotero

ZOTERO（http://www.zotero.org/）是美国乔治梅森大学历史与传播中心提供的免费开源软件，是目前功能最强大、最为流行的一款文献管理软件，绝大多数欧美大学图书馆都提供ZOTERO使用有关的简介。

https://www.bilibili.com/video/BV1zBpUeDEzM/

https://zotero-chinese.com/user-guide/plugins/zotero-gpt.html

https://github.com/MuiseDestiny/zotero-gpt

https://platform.moonshot.cn/console/account

logo zotero

4.12 AI与管理科学（运筹学）

http://kaiwu.city/index.php/solver

每个小组有 8个小木块和6 个大木块) ，

每张桌子需要2个大木块和2个小木块，

每把椅子需要1个大木块和2个小木块，

拼出1张桌子计20分，拼出1把椅子记15分，

问拼几张桌子、几把椅子，使总得分最高？

5.本地（无需联网）运行大语言模型LLaMa

http://kaiwu.city/index.php/ollama

文章信息: 作者： kaiwu; 日期：2024年11月15日; 点击数：87

1.关于示例数据集 (tourist satisfaction)

1.1调查问卷

http://kaiwu.city/openfiles/tourist_satisfaction_questionnaire_cn.pdf

No.	变量名	变量名标签
1	sid	编号
2	gender	性别
3	byear	出生年份
4	region	区域
5	income	月收入
6	expense	旅游支出
7	type3	旅游目的地类型
8	type2	旅游活动类型
9	thotel	酒店类型
10	sat1	景区sat
11	sat2	住宿sat
12	sat3	餐饮sat
13	sat4	交通sat
14	sat5	娱乐sat
15	sat6	购物sat
16	ri1	评论数量imp
17	ri2	评论的发表日期imp
18	ri3	评论相关性imp
19	ri4	评论中的正向评价imp
20	ri5	评论发布者的资信度imp
21	rp1	评论数量per
22	rp2	评论的发表日期per
23	rp3	评论相关性per
24	rp4	评论中的正向评价per
25	rp5	评论发布者的资信度per
26	te1	刺激感
27	te2	活动偏好
28	te3	旅游经历
29	te4	兴奋感
30	te5	寻求解放
31	te6	自由感
32	te7	新鲜
33	te8	恢复
34	zh1	人多不自在
35	zh2	不喜欢人际交往
36	zh3	不擅长人际交往
37	zh4	避免与人直接交流
38	zh5	外出很累
39	zh6	尽量不外出
40	zh7	外出麻烦
41	latitude	纬度
42	longitude	经度

Value labels gender
1 男
2 女.

Value labels region
1 华中
2 华东
3 华北
4 东北
5 西北
6 西南
7 华西.

Value labels type3
1 自然风光类型
2 历史文化类型
3 自然风光与历史文化混合的类型.

Value labels type2
1 观赏型
2 参与型.

Value labels thotel
1 经济型酒店
2 豪华型酒店
3 民宿
4 酒店式公寓.

1.2 通用数据格式 (没有 variable labels变量名标签和 value labels变量值标签)

http://kaiwu.city/openfiles/tourist.csv

或

http://kaiwu.city/openfiles/data_tourist_satisfaction_cn.xlsx

或

http://kaiwu.city/openfiles/data_tourist_cn.sav

2.使用python分析教学数据tourist

http://kaiwu.city/openfiles/tourist_CN_python2024a.ipynb

两个变量之间的关系relationship between two variables

types of variables (level of measurment)

https://statistics.laerd.com/statistical-guides/types-of-variable.php

https://www.thoughtco.com/independent-and-dependent-variable-examples-606828

https://datatab.net/tutorial/level-of-measurement

数据准备data preparation (data cleaning or data wrangling)

3.1 频数分析frequency Analysis（分类变量）

https://libguides.library.kent.edu/SPSS/FrequenciesCategorical

https://www.spss-tutorials.com/spss-frequencies-command/

https://datatab.net/tutorial/frequency-table

中文参考

https://zhuanlan.zhihu.com/p/108860781

3.2 列联表分析cross-table（分类变量）

https://libguides.library.kent.edu/SPSS/Crosstabs

中文参考

https://zhuanlan.zhihu.com/p/634975678

3.3 描述性统计分析Descriptive analysis（数值变量）

https://libguides.library.kent.edu/SPSS/Descriptives

https://www.spss-tutorials.com/spss-descriptives-command/

中文参考

https://blog.csdn.net/qq_42278015/article/details/119696576

4.1 卡方检验chi-square test

https://libguides.library.kent.edu/SPSS/ChiSquare

https://datatab.net/tutorial/chi-square-test

4.2 独立样本T检验independent sample t-test

https://libguides.library.kent.edu/SPSS/IndependentTTest

https://datatab.net/tutorial/unpaired-t-test

中文参考

https://blog.csdn.net/qq_51843109/article/details/123612791

4.3 单因素方差分析ANOVA

https://libguides.library.kent.edu/SPSS/OneWayANOVA

https://datatab.net/tutorial/anova

中文参考

https://zhuanlan.zhihu.com/p/448983174

4.4 相关分析correlation

https://libguides.library.kent.edu/SPSS/PearsonCorr

https://datatab.net/tutorial/correlation

中文参考

https://blog.csdn.net/nekonekoboom/article/details/116708114

4.5 逻辑回归分析logistic regression

https://www.spss-tutorials.com/logistic-regression/

https://datatab.net/tutorial/logistic-regression

中文参考

https://zhuanlan.zhihu.com/p/340480145

5.量表分析analysis for scales (measurement)

5.1 信度分析reliability analysis

https://www.spss-tutorials.com/cronbachs-alpha-in-spss/

https://www.spss-tutorials.com/spss-split-half-reliability/

https://datatab.net/tutorial/cronbachs-alpha

中文参考

https://www.zhihu.com/tardis/zm/art/270005975

5.2 探索性因子分析（用于效度分析）exploratory factor analysis (EFA)

https://www.spss-tutorials.com/spss-factor-analysis-tutorial/

https://www.spss-tutorials.com/spss-factor-analysis-intermediate-tutorial/

https://www.spss-tutorials.com/apa-reporting-factor-analysis/

https://datatab.net/tutorial/exploratory-factor-analysis

中文参考

https://www.zhihu.com/tardis/zm/art/270005975

6.输出分析结果export result

https://www.spss-tutorials.com/spss-output/

https://www.spss-tutorials.com/spss-apa-format-descriptives-tables/

中文参考

https://spss.mairuan.com/jiqiao/spss-wuxja.html

http://kaiwu.city/openfiles/academic_report_SPSS_CN.docx

文章信息: 作者： kaiwu; 日期：2024年11月14日; 点击数：103

simulated dataset (tourist satisfaction)

1. questionnaire

http://kaiwu.city/openfiles/tourist_satisfaction_questionnaire_en.docx

http://kaiwu.city/openfiles/tourist_satisfaction_questionnaire_en.pdf

No.	variable	variable labels
1	sid	Respondent ID
2	gender	gender
3	byear	Year of birth
4	region	region
5	income	monthly income
6	expense	daily expense
7	type3	Destination type: nature, culture and mix
8	type2	Destination type: sightseeing vs. participation
9	thotel	Types of hotel
10	sat1	satisfaction: scenery
11	sat2	satisfaction: hotel
12	sat3	satisfaction: food
13	sat4	satisfaction: transportation
14	sat5	satisfaction: travel agency
15	sat6	satisfaction: shopping
16	ri1	Importance: amount
17	ri2	Importance: publishing date
18	ri3	Importance: relevance
19	ri4	Importance: positive
20	ri5	Importance: credibility
21	rp1	Performance: amount
22	rp2	Performance: publishing date
23	rp3	Performance: relevance
24	rp4	Performance: positive
25	rp5	Performance: credibility
26	te1	thrill
27	te2	indulgence
28	te3	enjoyment
29	te4	excitement
30	te5	liberaty
31	te6	freedom
32	te7	refreshment
33	te8	revitalization
34	zh1	uncomfortable when there are many people
35	zh2	I dislike interpersonal communication
36	zh3	I’m not good at interpersonal communication
37	zh4	I try to avoid face-to-face communication
38	zh5	Going out is a tiring thing for me.
39	zh6	I try to avoid going out
40	zh7	It’s troublesome for me to go out.
41	latitude	latitude
42	longitude	longitude

value labels
Value labels gender
1	male
2	female.

Value labels region
1	Central China
2	East China
3	North China
4	Northeast China
5	Northwest China
6	Southwest China
7	West China.


Value labels type3
1	natural scenery
2	historical scenery
3	mixed scenery.

Value labels type2
1	sightseeing
2	participation.

Value labels thotel
1	budget hotel
2	luxury hotel
3	bed and breakfast
4	apartment hotel.

2. dataset

2.1 csv file

general purpose (without variable labels and value labels)

http://kaiwu.city/openfiles/tourist.csv

2.2 Excel file

http://kaiwu.city/openfiles/data_tourist_satisfaction_en.xlsx

2.3 SPSS file

http://kaiwu.city/openfiles/data_tourist_satisfaction_en.sav

3. python ipynb file

http://kaiwu.city/openfiles/tourist_EN2024Nov10.ipynb

4.results

http://kaiwu.city/openfiles/academic_report_SPSS_EN.docx

3.1 frequency Analysis（discrete variables）

https://libguides.library.kent.edu/SPSS/FrequenciesCategorical

https://www.spss-tutorials.com/spss-frequencies-command/

https://datatab.net/tutorial/frequency-table

3.2 cross-table（discrete variables）

https://libguides.library.kent.edu/SPSS/Crosstabs

3.3 Descriptive analysis（continuous variables）

https://libguides.library.kent.edu/SPSS/Descriptives

https://www.spss-tutorials.com/spss-descriptives-command/

3.4 custom table

https://www.ibm.com/docs/en/spss-statistics/saas?topic=statistics-custom-tables

http://kaiwu.city/index.php/spss-custom-table

4.relationship between two variables

types of variables (level of measurment)

https://statistics.laerd.com/statistical-guides/types-of-variable.php

https://www.thoughtco.com/independent-and-dependent-variable-examples-606828

https://datatab.net/tutorial/level-of-measurement

4.1 chi-square test

https://libguides.library.kent.edu/SPSS/ChiSquare

https://datatab.net/tutorial/chi-square-test

4.2 independent sample t-test

https://libguides.library.kent.edu/SPSS/IndependentTTest

https://datatab.net/tutorial/unpaired-t-test

4.3 ANOVA

https://libguides.library.kent.edu/SPSS/OneWayANOVA

https://datatab.net/tutorial/anova

4.4 correlation

https://libguides.library.kent.edu/SPSS/PearsonCorr

https://datatab.net/tutorial/correlation

4.5 logistic regression

https://www.spss-tutorials.com/logistic-regression/

https://datatab.net/tutorial/logistic-regression

5.analysis for scales (measurement)

5.1 reliability analysis

https://www.spss-tutorials.com/cronbachs-alpha-in-spss/

https://www.spss-tutorials.com/spss-split-half-reliability/

https://datatab.net/tutorial/cronbachs-alpha

5.2 exploratory factor analysis (EFA) for validity analysis

https://www.spss-tutorials.com/spss-factor-analysis-tutorial/

https://www.spss-tutorials.com/spss-factor-analysis-intermediate-tutorial/

https://www.spss-tutorials.com/apa-reporting-factor-analysis/

https://datatab.net/tutorial/exploratory-factor-analysis

6.export result

https://www.spss-tutorials.com/spss-output/

https://www.spss-tutorials.com/spss-apa-format-descriptives-tables/

文章信息: 作者： kaiwu; 日期：2024年11月11日; 点击数：584

http://kaiwu.city/openfiles/tourist_satisfaction_questionnaire_cn.pdf

1.python基础

python介绍

http://kaiwu.city/index.php/python

python推荐书籍

http://kaiwu.city/index.php/python-book

2.python软件的安装

http://kaiwu.city/index.php/python-vscode

http://kaiwu.city/openfiles/python-3.13.0-amd64.exe

http://kaiwu.city/openfiles/tourist.zip

3.使用python控制Excel

http://kaiwu.city/openfiles/hotel50python.xlsx

http://kaiwu.city/openfiles/python_excel50hotel.ipynb

文章信息: 作者： kaiwu; 日期：2024年11月06日; 点击数：104

1.introduction to python

about python

http://kaiwu.city/index.php/python

books on python

http://kaiwu.city/index.php/python-book

2.install python

python 3.13 you can download from official website

https://www.python.org/downloads/

http://kaiwu.city/openfiles/python-3.13.0-amd64.exe

http://kaiwu.city/index.php/python-vscode

3.python and Excel

http://kaiwu.city/openfiles/hotel50python_EN.xlsx

http://kaiwu.city/openfiles/python_excel50hotel_EN.ipynb

import openpyxl as xl;

# be sure to modify the folder where the files are stored.
# The names of the folders should not contain non-English characters and spaces.
filename ="D:/tdata/hotel50/hotel50python_EN.xlsx"
wb1 = xl.load_workbook(filename)
ws1 = wb1.worksheets[0]
mr = ws1.max_row
mc = ws1.max_column

# load the workbook
from openpyxl import Workbook

# create a new workbook
wb2 = Workbook()
ws2 = wb2.active

# The "i" represents the row number.
# create a workbook for the data from row 2 to row 50 respectively.
for i in range (2, mr + 1):
#The name of the new worksheet: Hotel Name
ws2.title = ws1.cell(row = i, column = 3).value

# j is the column number from 1 to 8:id, hotelid,Hotel Name,Rating,City,Address,Opening Year,Number of Rooms

for j in range (1, mc):
#Copy the first row of the source data, which is the header row, and use it as the first row of the new worksheet.
ws2.cell(row = 1, column = j).value = ws1.cell(row = 1, column = j).value

# Copy the data in the i-th row of the source data and use it as the 2nd row of the new worksheet.
ws2.cell(row = 2, column = j).value = ws1.cell(row = i, column = j).value

# Save the created Excel workbook.
wb2.save("D:/tdata/hotel50/" + ws2.title + ".xlsx")
# Output the result to indicate the work progress, specifically which worksheet has been completed already.

print('save No. %d Excel file。' % (i-1))

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生成式AI使用技巧

GTR_CN python02 tourist

GTR-EN python02

GTR_CN python01 basic

GTR-EN python01