인공지능/ 데이터 과학(통계) 저변확대

class: title-slide, left, bottom

# 인공지능/ 데이터 과학(통계) 저변확대
----
## **Digital Transformation, AI, Community**
### 이광춘(AIS)
### 2022-08-03

---
class: inverse, middle
name: digital-ranking

# 발표 개요

----

.pull-left[

4차 산업혁명은 .warmyellow[**디지털 전환(Digital Transformation)**] 이라는 
용어로 대체되고 있으며
전세계가 디지털 전환을 통해 새로운 도약을 준비하고 있어 다양한 지표를 통해
현재 대한민국 현재 위치를 데이터를 기반으로 객관적으로 파악한다.

디지털 기업의 핵심 경쟁력으로 파악되는 .black[**인공지능**]과 기술적 
기반이 되는 .black[**자동화**]와 .black[**딥러닝**]에 대해 전반적으로 조망한다.

.red[**인공지능 개발역량**]이 디지털 기업의 성패를 좌우하면서
인공지능 역량 내재화의 핵심문제로 부상되고 있는 
인력문제에 커뮤니티가 대안으로 급부상하고 있다.

]
 
.pull-right[   
.left[

1\. **[.warmyellow[디지털 전환]](#digital-ranking)** 
&nbsp;&nbsp;&nbsp;&nbsp; 1\.1\. 전통적 지표 
&nbsp;&nbsp;&nbsp;&nbsp; 1\.2\. 디지털 지표
 
2\. [인공지능](#deep-learning-toc) 
&nbsp;&nbsp;&nbsp;&nbsp; 2\.1\. 자동화 
&nbsp;&nbsp;&nbsp;&nbsp; 2\.2\. 인공지능 
&nbsp;&nbsp;&nbsp;&nbsp; 2\.3\. 딥러닝

3\. [딥러닝 / 커뮤니티](#deep-learning-community) 
&nbsp;&nbsp;&nbsp;&nbsp; 3\.1\. 소프트웨어 2.0 
&nbsp;&nbsp;&nbsp;&nbsp; 3\.2\. 하드웨어 / MLOps 
&nbsp;&nbsp;&nbsp;&nbsp; 3\.3\. 커뮤니티

4\. [마무리](#goodbye) 
]
]

---
name: automation-tax
# 한국데이터기술진흥협회 기술 세미나

.center[

]

---
name: esg-country
# 전통적 지표

.center[
<img src="fig/esg-country-classic.png" />
]

.footnote[
- [위키백과 OECD](https://en.wikipedia.org/wiki/OECD)
]

---
name: esg-country-classic
# 전통 지표 기준 강대국

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---
name: esg-country-extended
# 확장 전통 지표

.center[
<img src="fig/esg-country-extension.png" />
]

.footnote[
- [위키백과 OECD](https://en.wikipedia.org/wiki/OECD)
]

---
name: esg-country-extended-table
# 확장 지표 기준 선진국

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---
name: esg-digital-country-start
# 디지털 강대국 지표

.center[
![](fig/oecd-dataset.png)
]

- [디지털 정부 순위](https://www.oecd-ilibrary.org/deliver/4de9f5bb-en.pdf?itemId=%2Fcontent%2Fpaper%2F4de9f5bb-en&mimeType=pdf)
- [정부 인공지능 준비지수](https://www.oxfordinsights.com/government-ai-readiness-index-2020)
- [데이터 경제 순위](https://hbr.org/2019/01/which-countries-are-leading-the-data-economy)
- [산업용 로봇 밀도](https://ifr.org/downloads/press2018/Robot_Density_by_country_WorldRobotics2020_graph.jpg)
- [세계 경쟁력 보고서](https://reports.weforum.org/global-competitiveness-report-2019/competitiveness-rankings/)
- [IMD 세계 경쟁력 연감](https://www.imd.org/wcc/world-competitiveness-center-rankings/world-digital-competitiveness-rankings-2020/)

---
name: esg-digital-government
# 디지털 강대국 지표 - 디지털 정부 순위

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---
name: esg-digital-ai-government
# 디지털 강대국 지표 - 인공지능 준비지수

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---
name: esg-digital-data-economy
# 디지털 강대국 지표 - 데이터 경제

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---
name: esg-digital-robot
# 디지털 강대국 지표 - 산업용 로봇

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---
name: esg-digital-competitiveness
# 디지털 강대국 지표 - WEF 세계 경쟁력

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---
name: esg-digital-imd
# 디지털 강대국 지표 - IMD 디지털 경쟁력

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---
name: esg-digital-summary
# 디지털 강대국 지표 - 종합

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---
name: esg-digital-visualization
# 디지털 강대국 지표 - 종합 시각화

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---
class: inverse, middle
name: deep-learning-toc

# 발표 개요

----

.pull-left[

디지털 기업의 핵심 경쟁력으로 파악되는 .black[**인공지능**]과 기술적 
기반이 되는 .black[**자동화**]와 .black[**딥러닝**]에 대해 전반적으로 조망한다.

]
 
.pull-right[   
.left[

1\. [디지털 전환](#digital-ranking) 
&nbsp;&nbsp;&nbsp;&nbsp; 1\.1\. 전통적 지표 
&nbsp;&nbsp;&nbsp;&nbsp; 1\.2\. 디지털 지표
 
2\. **[.warmyellow[인공지능]](#deep-learning)** 
&nbsp;&nbsp;&nbsp;&nbsp; 2\.1\. 자동화 
&nbsp;&nbsp;&nbsp;&nbsp; 2\.2\. 인공지능 
&nbsp;&nbsp;&nbsp;&nbsp; 2\.3\. 딥러닝

4\. [마무리](#goodbye) 
]
]

---
name: automation-tax
# 세무사 업무 변천과정

.center[

![세무 업무 변천사](fig/tax-preparation.png)

]

.footnote[
[이광춘 (2019-12-04), "자동화(Automation) - ~~로봇~~ 자동화가 빼앗는 일자리 그리고 나", 분당 서현 청소년 수련관 ](https://statkclee.github.io/ds-authoring/ds-bundang-2019.html)
]

---
name: automation-productivity-gap
# 생산성과 임금 격차

<div id="htmlwidget-631a1c70c4b3bead8360" style="width:100%;height:67%;" class="plotly html-widget"></div>
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metric: gap","Year: 1974 value: 0.0678 metric: gap","Year: 1975 value: 0.1125 metric: gap","Year: 1976 value: 0.1424 metric: gap","Year: 1977 value: 0.1323 metric: gap","Year: 1978 value: 0.1261 metric: gap","Year: 1979 value: 0.1486 metric: gap","Year: 1980 value: 0.1872 metric: gap","Year: 1981 value: 0.2314 metric: gap","Year: 1982 value: 0.2067 metric: gap","Year: 1983 value: 0.2602 metric: gap","Year: 1984 value: 0.3318 metric: gap","Year: 1985 value: 0.3747 metric: gap","Year: 1986 value: 0.4103 metric: gap","Year: 1987 value: 0.4413 metric: gap","Year: 1988 value: 0.4799 metric: gap","Year: 1989 value: 0.5019 metric: gap","Year: 1990 value: 0.5458 metric: gap","Year: 1991 value: 0.5648 metric: gap","Year: 1992 value: 0.6428 metric: gap","Year: 1993 value: 0.6505 metric: gap","Year: 1994 value: 0.6665 metric: gap","Year: 1995 value: 0.6883 metric: gap","Year: 1996 value: 0.7337 metric: gap","Year: 1997 value: 0.7585 metric: gap","Year: 1998 value: 0.7694 metric: gap","Year: 1999 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value: 0.6470 metric: Hourly compensation","Year: 1967 value: 0.6668 metric: Hourly compensation","Year: 1968 value: 0.7105 metric: Hourly compensation","Year: 1969 value: 0.7439 metric: Hourly compensation","Year: 1970 value: 0.7681 metric: Hourly compensation","Year: 1971 value: 0.8166 metric: Hourly compensation","Year: 1972 value: 0.9134 metric: Hourly compensation","Year: 1973 value: 0.9096 metric: Hourly compensation","Year: 1974 value: 0.8705 metric: Hourly compensation","Year: 1975 value: 0.8686 metric: Hourly compensation","Year: 1976 value: 0.8935 metric: Hourly compensation","Year: 1977 value: 0.9282 metric: Hourly compensation","Year: 1978 value: 0.9566 metric: Hourly compensation","Year: 1979 value: 0.9325 metric: Hourly compensation","Year: 1980 value: 0.8805 metric: Hourly compensation","Year: 1981 value: 0.8736 metric: Hourly compensation","Year: 1982 value: 0.8770 metric: Hourly compensation","Year: 1983 value: 0.8849 metric: Hourly compensation","Year: 1984 value: 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.footnote[
[Economic Policy Institute, "The Productivity - Pay Gap", July 2019](https://www.epi.org/productivity-pay-gap/)
]

---
name: automation-sourcing-strategy
# 소싱전략의 변화

.center[

![소싱 전략의 변화](fig/the-end-of-outsourcing.png)

]

.footnote[
자료출처: Kinetics consulting services / Automation Anywhere 
]

---
name: automation-apollo
# 아폴로 우주선

.center[

![아폴로 계획에 대한 역할 구분](fig/man-machine-role-allocation.png)

]

.footnote[

[Mary (Missy) Cummings (2014), "Man versus Machine or Man + Machine?", Duke University and MIT](https://hal.pratt.duke.edu/sites/hal.pratt.duke.edu/files/u10/IS-29-05-Expert%20Opinion[1]_0.pdf)

]

---
name: automation-role-allocation
# 인간과 기계 업무분장

.center[

]

.footnote[

[Mary (Missy) Cummings (2017), "Informing Autonomous System Design Through the Lens of Skill-, Rule-, and Knowledge-Based Behaviors", Duke University](https://hal.pratt.duke.edu/sites/hal.pratt.duke.edu/files/u10/IS-29-05-Expert%20Opinion[1]_0.pdf)

]

---
name: automation-jobs
# Human Supervisory Control

.center[

![human-supervisory-control](fig/human-supervisory-control.png)

]

.footnote[

]

---
name: automation-paradox
# 모라벡의 역설(Moravec’s paradox)

.center[

]

.footnote[
미국 카네기 멜론 대학 (CMU) 로봇 공학자 한스 모라벡(Hans Moravec)이 1970년대에 ‘it is comparatively easy to make computers exhibit adult level performance on intelligence tests or playing checkers, and difficult or impossible to give them the skills of a one-year-old when it comes to perception and mobility’라는 표현으로 컴퓨터와 인간의 능력 차이를 역설적으로 표현하였다.
]

---
name: automation-comparison
# 중국어 방 (Chinese room)

.pull-left[
![중국어 방 주장을 모사한 도해](fig/chinese-room-argument.jpg)
]

.pull-right[

]

.footnote[
[중국어 방 역설 (Chinese room argument) - 대체 누가 중국어를 이해하고 있는가?](http://ko.experiments.wikidok.net/wp-d/592f718da44f1a4153e80611/View)
]

---
name: dl-timeline
# 기계학습 분류

.center[
<img src="fig/machine-learning-classification.png" alt="기계학습 분류" width="100%" />
]

---
name: dl-timeline
# 신경망 모형 여정

.center[
<img src="fig/dl-timeline.png" alt="timeline" width="100%" />
]

.footnote[
Liangqu Long, Xiangming Zeng (2022), "Beginning Deep Learning with TensorFlow: Work with Keras, MNIST Data Sets, and Advanced Neural Networks", Apress
]

---
name: dl-applicatin
# 딥러닝 활용사례

.panelset[

.panel[.panel-name[이미지 데이터]
.center[
<img src="fig/dl-image.png" alt="이미지 데이터" width="100%" />
]
]

.panel[.panel-name[텍스트 데이터]
.center[
<img src="fig/dl-text.png" alt="텍스트 데이터" width="100%" />
]
]

.panel[.panel-name[정형 데이터]
.center[
<img src="fig/dl-csv.png" alt="정형데이터" width="100%" />
]
]

.panel[.panel-name[...]
]

]

---
name: human-performance
# 딥러닝 알고리즘 성능 - 이미지

.footnote[
[Image Classification on ImageNet](https://paperswithcode.com/sota/image-classification-on-imagenet)
]

---
name: human-performance-nlp
# 딥러닝 알고리즘 성능 - 텍스트

.footnote[
[SQuAD2.0 - The Stanford Question Answering Dataset](https://rajpurkar.github.io/SQuAD-explorer/)
]

---
class: inverse, middle
name: deep-learning-community

# 발표 개요

----

.pull-left[

디지털 기업의 핵심 경쟁력으로 파악되는 .black[**인공지능**]과 기술적 
기반이 되는 .black[**자동화**]와 .black[**딥러닝**]에 대해 전반적으로 조망한다.

]
 
.pull-right[   
.left[

1\. [디지털 전환](#digital-ranking) 
&nbsp;&nbsp;&nbsp;&nbsp; 1\.1\. 전통적 지표 
&nbsp;&nbsp;&nbsp;&nbsp; 1\.2\. 디지털 지표
 
2\. [인공지능](#deep-learning-toc) 
&nbsp;&nbsp;&nbsp;&nbsp; 2\.1\. 자동화 
&nbsp;&nbsp;&nbsp;&nbsp; 2\.2\. 인공지능 
&nbsp;&nbsp;&nbsp;&nbsp; 2\.3\. 딥러닝

3\. **[.warmyellow[딥러닝 / 커뮤니티]](#deep-learning-community)** 
&nbsp;&nbsp;&nbsp;&nbsp; 3\.1\. 소프트웨어 2.0 
&nbsp;&nbsp;&nbsp;&nbsp; 3\.2\. 하드웨어 / MLOps 
&nbsp;&nbsp;&nbsp;&nbsp; 3\.3\. 커뮤니티

4\. [마무리](#goodbye) 
]
]

---
name: deep-learning-vs-software
# 소프트웨어 1.0 vs. 딥러닝 모형

.panelset[

.panel[.panel-name[소프트웨어 x.0]
.center[
![](fig/software-versions.png)
]
]

.panel[.panel-name[Andrej Karpathy]
.center[
![](fig/ai/Andrej-Karpathy.png)
]
]

]

.footnote[
Domingos, Pedro. The master algorithm: How the quest for the ultimate learning machine will remake our world. Basic Books, 2015.
]

---
name: hidden-technical-debt
# 기계학습(딥러닝) 기여분

.center[
![](fig/hidden-technical-debt.png)
]

.footnote[
Sculley, David, et al. "Hidden technical debt in machine learning systems." Advances in neural information processing systems 28 (2015).
]

---
name: deep-learning-end2end
# 딥러닝 모형 개발 범위

.center[
<img src = "fig/end2end-deep-learning.png" width = "87%">
]

.footnote[
Eli Stevens, Luca Antiga, and Thomas Viehmann (2020), "Deep Learning with PyTorch", Manning.
]

---
name: dl-hardware
# 딥러닝 하드웨어

.panelset[

.panel[.panel-name[윈텔]
.center[
<img src="fig/ai/wintel-architecture.png" alt="윈텔" width="80%" />
]
]

.panel[.panel-name['83년 산업계]
.center[
<img src="fig/ai/computer-industry-1983.png" alt="1983년 컴퓨터 산업" width="80%" />
]
]

.panel[.panel-name['02년 산업계]
.center[
<img src="fig/ai/computer-industry-2002.png" alt="2002년 컴퓨터 산업" width="70%" />
]
]

.panel[.panel-name[칩]
.center[
<img src="fig/ai/semi-conductor-pyramid.png" alt="2022년 반도체칩" width="60%" />
]
]

.panel[.panel-name[원가]

.center[
<img src="fig/dl_svr_g.png" alt="딥러닝 서버 원가구조" width="80%" />
]
]

.panel[.panel-name[원가상세]

<div id="htmlwidget-b7ca46171bc93a43307f" class="reactable html-widget" style="width:auto;height:auto;"></div>
<script type="application/json" data-for="htmlwidget-b7ca46171bc93a43307f">{"x":{"tag":{"name":"Reactable","attribs":{"data":{"구분":["워크스테이션","워크스테이션","워크스테이션","워크스테이션","워크스테이션","워크스테이션","워크스테이션","워크스테이션","워크스테이션"],"품명":["VGA","CPU","RAM","M/B","POWER","쿨러","SSD","CASE","HDD"],"제품사양":["NVIDIA 지포스 갤럭시 RTX 3090 SG D6X 24GB","인텔 코어X I9 10980XE 캐스케이드레이크","삼성 DDR4-2933 32GB (총 256GB )","ASUS PRIME X299 SAGE STCOM","EVGA SUPERNOVA 2000G+ 80PLUS GOLD","NZXT KRAKEN X63 ( 3열수랭)","삼성 980 PRO M.2 NVMe 1TB","CORSAIR OBSISIAN 750D AIRFLOWER","WD RED 4TB"],"수량":[2,1,8,1,1,1,1,1,1],"단가":[2000000,1500000,150000,1000000,500000,250000,250000,200000,150000],"가격":[4000000,1500000,1200000,1000000,500000,250000,250000,200000,150000],"비율":[0.441988950276243,0.165745856353591,0.132596685082873,0.110497237569061,0.0552486187845304,0.0276243093922652,0.0276243093922652,0.0220994475138122,0.0165745856353591],"누적비율":[0.441988950276243,0.607734806629834,0.740331491712707,0.850828729281768,0.906077348066298,0.933701657458564,0.961325966850829,0.983425414364641,1]},"columns":[{"accessor":"구분","name":"구분","type":"character"},{"accessor":"품명","name":"품명","type":"character","width":80},{"accessor":"제품사양","name":"제품사양","type":"character","width":350},{"accessor":"수량","name":"수량","type":"numeric","width":50},{"accessor":"단가","name":"단가","type":"numeric","format":{"cell":{"separators":true},"aggregated":{"separators":true}}},{"accessor":"가격","name":"가격","type":"numeric","aggregate":"sum","format":{"cell":{"separators":true},"aggregated":{"separators":true}}},{"accessor":"비율","name":"비율","type":"numeric","format":{"cell":{"digits":1,"percent":true},"aggregated":{"digits":1,"percent":true}},"width":70},{"accessor":"누적비율","name":"누적비율","type":"numeric","format":{"cell":{"digits":1,"percent":true},"aggregated":{"digits":1,"percent":true}},"width":70}],"pivotBy":["구분"],"defaultPageSize":10,"showPageSizeOptions":true,"pageSizeOptions":[10,25,50,100],"paginationType":"numbers","showPageInfo":true,"minRows":1,"highlight":true,"bordered":true,"striped":true,"dataKey":"81233c376f32b26128205f0cc63fcb69"},"children":[]},"class":"reactR_markup"},"evals":[],"jsHooks":[]}</script>

]

.panel[.panel-name[클라우드]
.center[
<img src="fig/ai/dl-private-cloud.png" alt="딥러닝 클라우드" width="60%" />
]
]

]

.footnote[
- [한국 R 사용자회, "20분만에 끝내는 딥러닝 - 딥러닝 하드웨어"](https://aispiration.com/united-states/40_hardware.html)
]

---
name: dl-devops
# 딥러닝 제품과 서비스

.panelset[

.panel[.panel-name[작업흐름]
.center[
 
<img src="fig/ai/dl-workflow.jpg" alt="딥러닝 작업흐름" width="100%" />
]
]

.panel[.panel-name[AutoML]
.center[
 
<img src="fig/ai/autoML.jpg" alt="autoML" width="100%" />
]
]

.panel[.panel-name[Ludwig]
.center[
 
<img src="fig/ai/ludwig-encoding-decoding.png" alt="Ludwig" width="70%" />
]
]

.panel[.panel-name[yaml]
.center[
 
<img src="fig/ai/ludwig_legos_unanimated.gif" alt="ludwig-yaml" width="100%" />
]
]

.panel[.panel-name[DVC]
.center[
 
<iframe width="560" height="315" src="https://www.youtube.com/embed/DYeVI-QrHGI" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
]
]

.panel[.panel-name[DevOps]
.center[
 
<img src="fig/ai/software-devops.png" alt="devops" width="100%" />
]
]

.panel[.panel-name[MLOps]
.center[
 
<img src="fig/ai/ml-devlops.png" alt="ml-devops" width="100%" />
]
]

.panel[.panel-name[전이학습]
.center[
 
<img src="fig/ai/transfer-learning.png" alt="ml-devops" width="100%" />
]
]

]

---
name: dl-community
# 오픈 AI 커뮤니티와 개발자

.panelset[

.panel[.panel-name[데이터 사이언스]
.center[
 
<img src="fig/ai/r-communities.png" alt="데이터 사이언스 커뮤니티" width="70%" />
]
]

.panel[.panel-name[한국데이터기술진흥협회]
.center[
<img src="fig/diva-agenda.png" alt="한국데이터기술진흥협회" width="40%" />
]
]

.panel[.panel-name[대전과학문화거점센터]

> 이 프로그램은 과학기술진흥기금 및 복권기금의 재원으로 운영되고,과학기술정보통신부와 한국과학창의재단의 지원을 받아 수행된 성과물로 우리나라의 과학기술 발전과 사회적 가치 증진에 기여하고 있습니다.

> R Consortium과 한밭대학교 창업경영학과에서 장소를 지원해 주셨으며, 과학기술정보통신부, 한국과학창의재단, 대전광역시, 대전관광공사, **대전과학문화거점센터** 지원해 주셨습니다.

.center[
 
<img src="fig/ai/sponsors.png" alt="대전과학문화거점센터" width="80%" />
]
]

.panel[.panel-name[글로벌 커뮤니티]

.center[
<img src="fig/ai/data-carpentry.jpg" alt="데이터 카펜트리" width="45%" />
]

- 케펜트리 웹사이트: https://carpentries.org/
- 참가신청: https://www.eventbrite.com/e/at-tickets-390464669337
- 워크샵 웹사이트: http://aispiration.com/2022-08-18-hanbat/

]

.footnote[
- [한국R사용자회](https://r2bit.com/) / [대덕이노폴리스벤처협회](https://www.diva.or.kr/)
]

---
name: daejeon-goodbye
class: middle, inverse

.pull-left[
# **경청해 주셔서 감사합니다.**
 
## AIS & [한국 R 사용자회](https://r2bit.com/)

]

.pull-right[
.right[

]
]