2014/11/18 § Leave a comment
2014/11/13 § 2 Comments
2014/10/11 § Leave a comment
“United States Central Command’s Strategic Role in Our Nation’s Defense”이란 주제로 General Lloyd J. Austin의 강의가 있었다. 중동 지방의 상황에 대해 브리핑하고 미국의 관점에 대해 이야기 해주었다. 그 후에 Q&A 시간이 있었는데, 많은 사람들이 질문했다.
CMU에서는 다양한 톡과 강의가 자주 열린다. 내 분야와 딱히 관련이 없는 것도 많지만, 재미있어 보이고 시간이 되면 가급적 참석해서 듣고 있다. 왜 원래 아기들한테도 최대한 다양한 인풋을 주는 게 좋다잖는가ㅋㅋ
2014/10/03 § Leave a comment
이렇게 다시 만나니 감회가 새롭군 ㅋㅋ
IBM에서는 전산학 관점에서, 이제 programmable computing 시대에서 cognitive computing 시대로 넘어가고 있다고 본다. Watson은 question answering의 완성이라기 보다는, 이 새로운 시대를 시작하는 신호탄에 불과하다. IBM이 생각하고 있는 것은 Watson을 기반으로 한 다양한 applications, 예를 들어 healthcare, marketing 등에 접목되어 사람을 도와주는 시스템이다. 단순히 QA 시스템을 넘어서 이제는 reasoning & debating 시스템을 개발하고 있고, 텍스트를 넘어서 computer vision 등도 접목시키고 있다. 애당초에 이런 큰 비전을 가지고 Watson을 개발한 건지는 모르겠으나, 어쨌든 대단하다는 생각.
2014/10/02 § 2 Comments
Carnegie Mellon Leads New NSF Project to Improve Learning
Carnegie Mellon University will lead a five-year, $5 million early implementation project sponsored by the National Science Foundation to improve educational outcomes and advance the science of learning by creating a large, distributed infrastructure called LearnSphere that will securely store data on how students learn.
NSF Award Abstract
Awarded Amount to Date: $4,830,819.00
Investigator(s): Ken Koedinger Ken.Koedinger@cs.cmu.edu (Principal Investigator), John Stamper (Co-Principal Investigator), Carolyn Rose (Co-Principal Investigator)
This project is creating a community software infrastructure, called LearnSphere, that supports sharing, analysis, and collaboration across the wide variety of educational data. LearnSphere supports researchers as they improve their understanding of human learning. It also helps course developers and instructors improve teaching and learning through data-driven course redesign. The goal is to transform learning science and engineering through a large, distributed data infrastructure, and develop the capacity for course developers, instructors, and learning engineers to make use of it.
LearnSphere maintains a central store of metadata about what datasets exist, but also has distributed features allowing contributors control over access to their own data. It provides a hub to link many communities of educational researchers, provides a repository for researchers to store their data, and provides an open analytic method library and workflow-authoring environment for researchers to build models and run them across datasets.
The research team has extensive experience not only in using educational data mining to make discoveries and improve student outcomes, but also in the creation of educational data infrastructures. They have developed the DataShop infrastructure, which is currently the largest open repository of educational technology data including over 550 datasets. A newer data infrastructure, MOOCdb, is being developed to store and analyze Massively Open Online Course (MOOC) data. The Open Learning Initiative has produced data stored in DataShop for many years and is expanding into the MOOC space. Dialogue-based tutoring systems and student affect sensors are producing new kinds of data that are being added to LearnSphere. The researchers are further improving data collection infrastructure in MOOCs especially by adding platform components for massive multi-factor online experiments. The project is also creating new methods for data integration, discourse data storage and analytics, and new algorithms for automated discovery, as well as new learning science discoveries that result from these algorithms.
By integrating these building blocks in LearnSphere, the project will facilitate cross-modality and cross-domain educational data analysis that is not possible today.