Corporate members of NYC Media Lab take part in annual open seed projects, a way to immediately plug into the cutting-edge talent and research taking place across NYC’s universities. We invite a high degree of participation from member companies, allowing them the ability to shape the questions and goals surrounding each collaborative project.
Executives, faculty and students from NYC Media Lab’s projects will share their innovations from the past year.
Verizon's Connected Futures Challenge
Verizon's Connected Futures Research and Prototyping Challenge was a program for university teams to focus on connected devices, hardware, journalism, and virtual reality. Fourteen projects from faculty, researchers and students from Columbia University, Cornell Tech, The New School, New York University and Pratt Institute participated in this program. Christian Guirnalda, Director, Open Innovation at Verizon will share how the program was conceived and executed to develop relationships with universities focusing on several themes such as connected home, empowering citizen journalism, and virtual reality and augmented reality. University team leaders will present their projects.
In the spirit of exploring rapid prototyping, Viacom selected 6 Fellows from across NYC universities to develop new, immersive VR stories. The Fellowship program took place over 8 weeks in the summer of 2016. The Viacom New Experience Team (NEXT) provided mentorship to the Fellows and the program produced 5 new VR experiences. In a panel conversation, Viacom NEXT and several Fellows will share the technical lessons learned from eight weeks of iteration and production for the HTC Vive and Oculus.
HEARST | APPLICATIONS OF DEEP LEARNING IN MEDIA
In a current project, Hearst is working with New York University to understand and model the linkages between the text of articles and the images contained in the articles. To preview their workshop, Rahel and Kyunghyun will provide an overview of the progress they have made in using techniques in deep learning and machine learning, especially computer vision/image recognition/object detection, to experiment with building a recommendation/image retrieval engine that will automatically suggest images given new text content.