[my note] "The Best Recruiter at Google" by Laszlo Bock (SVP, People Operations at Google) at Talent Connect San Francisco 2014

<video source: link> 

Below is what I learned from this talk; almost all of them are not my words but Laszlo's. Bolded texts are personal key lessons from this talk.

1. set a high bar for quality … and never compromise 

2. assess candidates objectively… science FTW

• no names before resume screening
• 4 criteria of a Google interview
• general cognitive ability: how well someone can solve problems, how curious they are, and how fast they can pick new things up
• leadership: no titles and management necessary; emergent leadership!
• when they see a problem when they are a member of a team, and they see the problem and step in, help solve the problem but just as importantly as soon as the problem is resolved, they step back out; they are willing to relinquish power
• Googleyness: are they comfortable with ambiguity, do they have intellectual humility (i.e. able to say, "I was wrong" when presented with new data and change their positions), bring something new and different to our mix/organization 
• role-related knowledge: do they actually have skills and knowledge to do the job we’re hiring them for
• provide clear criteria to look for
• define what best, mediocre and bad examples
• structured interviews: consistent set of questions
• situational questions: hypothetical questions ("what would you do, why did you do that, what else would you do, why would you take other actions")
• behavioral questions: describe prior achievements
• "give me an example of an incredibly difficult problem you solved, tell me more"
• what candidates consider challenging or exemplifying that attributes and how to directly relate back to the job 

3. give candidates a reason to join

[lecture] Data Analysis on Clouds by Jun Sup Lee (이준섭, KT/SW개발센터장)

Title	Data Analysis on Clouds
Place	KAIST Dogok Software Grad School Chin’s AMP Hall 103-ho
Time	October 30, 2014 5PM~6PM
Speaker	Jun Sup Lee (이준섭, KT/SW개발센터장)
Organizer	KAIST Software Graduate Program
Focus	KT's cloud solution,

I.       Message

1. impact of disruptive technology: after industrial revolution, many people lost jobs; likewise, after self-driving car, many taxi drivers may lose jobs as well
2. reason behind Google's Map business involvement: Google wanted to know where people live perhaps because the company local marketing piece of ad was missing in its product and revenue stream portfolio
3. data analysis process and examples on clouds
1. general rule of thumb: create summary-> mine data-> select machine learning method -> ?
2. ad display logic: cluster similar behaviors when ads are shown to users-> display similar ads matching the cluster
3. movie recommendation logic: vector -> choose similar users-> recommend similar movies
4. lesson from Melon: need to discern data's end results such as how users would response to product of data analysis before developing data analysis product e.g. users only care for top 10 rankings instead of relevant music to their tastes
4. new lingo: ETL (extract, transform and load)

II.      My Takeaway

1. need to work on jobs that will survive in 10 to 20 years
2. think end results before development

[lecture] 디지털 교육의 현황과 미래 - KnowRe의 미국시장 진출 사례를 중심으로”<-(translation: the present and future of digital education - a focused example on KnowRe's U.S. market launch)

Title	"디지털 교육의 현황과 미래 - KnowRe의 미국시장 진출 사례를 중심으로” (translation: the present and future of digital education - a focused example on KnowRe's U.S. market launch)
Place	KAIST Dogok Software Grad School Chin’s AMP Hall 103-ho
Time	October 23, 2014 5PM~6PM
Speaker	김서준(Simon Seojoon Kim),  노리(KnowRe)/부사장(Co-Founder & Chief Product Officer)
Organizer	KAIST Software Graduate Program
Focus	digital education

I.       Message

1. In math, each problem acts like a stepping stone for the next place
1. “Each problem that I solved became a rule, which served afterwards to solve other problems.”- Descartes
2. finding a pattern: knowledge unit, example and explanation video for each unit, content matrix for entire knowledge unit
1. knowledge unit (interpretive; formulaic: formula; computational)
2. knowledge chain
3. math problem
4. knowledge matrix
3. business development process
1. market research: market penetration in the U.S., blended learning (e.g. 47% in 2014, expecting 98% in 2020)
2. market voice: received feedback at NCTM 2012 Philadelphia
1. positive feedback: met 200 teachers, received positive feedback “want to use at school” from 84% of interviewed teachers
3. hiring: interviewed and found a US rep person at NCTM 2012 Philadelphia
4. investment: K Startup funding -> series A round funding from SoftBank Ventures Korea
5. business type: game company-like business
1. content gathering takes first before anything else
6. recognition
1. education field:  NYC DOE Gap App Challenge (1st place)
2. media: Fast Company, the world’s top 10 most innovative companies in education
7. pilot program: selected 37 out of 150 school applications, receiving 87% satisfaction
8. risk management: dealt with teachers’ concern on “no-teacher needed?” question by providing improved engagement between through dashboard (i.e. check progress status)
4. reason for choosing U.S. market: learned that societal consensus for the solution is important
5. vision for digital edu: being able to provide contents customized for individual students
6. market expansion plan: global market opportunity of B2B (schools) and B2C (students/parents) from the U.S. to East Asia
7. business model: focus on schools in the U.S., direct-consumers in Korea, Japan and China
8. role of class: information sharing and supplemental support from teachers

II.      My Takeaway

1. Like solving a math problem or learning anything, each problem/challenge acts like a stepping stone for the next progress; thus, work hard to master each step as they approach
2. KnowRe's attempt to create a content matrix platform where knowledge of STEM (science, technology, engineering, and math) is broken down to small knowledge unit and each unit is connected to each other can become the next Google's Knowledge Graph initiative to organize the world's information

[info] 정보 패턴 분석=> 연관성 도출=> 의사 결정 활용=> OKR

<source: http://premium.chosun.com/site/data/html_dir/2014/10/19/2014101902729.html?outlink=facebook> 
정보의 패턴을 분석해 연관성을 가져오고, 이를 의사 결정에 활용하는 빅 데이터 활용 좋아요.
1. 미국 샌프란시스코시 범죄 지도: 8년간 발생한 200여 종 범죄로부터 범죄 유형과 발생 지역 분석 => 경찰력 효율적 배치 => 범죄 예측 정확도 70%
2. 구글의 독감 트렌드: 전 세계 이용자 독감 관련 검색 실태 분석 => 해당 국가/지역 실제 독감 창궐 시기 예측 => 독감 예측과 실제 발생 일치
3. 빅 데이터를 활용한 식중독 예방: 12년간 부산,울산,경남 식중독 발생 이력, 원인균, 지역, 발생 음식, 날씨 분석 => 기숙사 유무, 지하수 사용 여부, 쓰레기 소각장 식당 거리 자료에 따른 학교 선정 예방 컨설팅 => 올 상반기에 작년 대비 식중독 환자 수 69.2% 줌

[HCI Trends 02 2014] for inclusive design, be the disabled instead of think disabled

Lesson learned from reading HCI Trends 2014 02



title: 장애인과 UX
author: 문태경 (이언인사이트 수석연구원, moontk@aeoninsight.co.kr)
my takeaway: for inclusive design, people involved with product development need to actually role-play the disabled instead of thinking as a disabled
"내가 장애인이라면 이라는 생각을 갖고 개발하지 말고요, 장애인이 돼서 개발을 하면 쉬울 거예요. 저 사람은 목 밖에 안 움직이니까 이렇게 하면 쉽지 않을까가 아니라, 직접 목만 움직이면서 입에다가 터치 펜을 물고 해 보면 어떤 게 더 편하다, 어떤 게 더 불편하다, 정확하게 알 수 있겠죠." - 지체 장애그룹 FGI 중에서