융합적 관점에서 본 소프트웨어 공학의 미래

Roger’s 5 Factors in Adoption

•Relative Advantage: How improved an innovation is over the previous generation

•Compatibility: The level of compatibility that an innovation has to be assimilated into an individual’s life.

•Complexity or Simplicity: If the innovation is perceived as complicated or difficult to use, an individual is unlikely to adopt it.

•Trialability: How easily an innovation may be experimented. If a user is able to test an innovation, the individual will be more likely to adopt it.

•Observability: The extent that an innovation is visible to others. An innovation that is more visible will drive communication among the individual’s peers and personal networks and will in turn create more positive or negative reactions.

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Deep Learning World Challenge

New Algorithms

 

 

 

Dropout

• Randomly drop units
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• Reducing over-fitting
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• Simple and efficient

 

BIG DATA

ILSVRC : Image Classification  

 

 

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인공지능의 한계와 가능성

스마트폰을 위한 인공지능

스마트폰북



인공지능의한계와가능성

•환원주의(Reductionism) vs. 행동주의(Behaviorism)
–괴델의불완전성정리
–새로운패러다임의필요

•양자컴퓨팅, emergent computing, 인공생명

•세계인공지능시장규모
–2015년약1270억달러2017년약1650억달러로예상(IDC)
–2025년인공지능을통한‘지식노동자동화’의파급효과가연간5조2000억~6조7000억달러에달할것으로전망(맥킨지)

•일자리문제
–축소: 매뉴얼에기반한직종, 반복성높은직종, 일부전문서비스직종
–확장: 면대면직업, 창의적/예술적/감성적직업, 복잡성높은육체적직업
–기대: 노동시간줄고, 고용구조변화

인공지능R&D 방향

•One Hundred Year Study on Artificial Intelligence (AI100)
–100-year effort to study critical issues in the design and use of AI systems, including their economic and social impact

•Allen Institute for Artificial Intelligence (AI2)
–contribute to humanity through high-impact AI research and engineering by constructing AI systems with reasoning, learning and reading capabilities

•100 billion yen for 10 years for AI R&D in Tokyo
–establish a system to aim realization of the production revolution of advanced medical and factories

•우리의인공지능R&D 방향
–학제간융합연구: 인간연구+ 융합연구+ 컴퓨팅기술
–경제/사회적고찰: 일자리, 인간의존엄성
–장기적꾸준한투자/연구개발필요

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