Amazon Research Awards Honor Outstanding Academic Projects in Artificial Intelligence
Broad range of applications demonstrates the diversity of research for AI technology solutions
49 scientific groups from 28 institutions will be awarded with a total of 3.7 million US-Dollars
These are some of the projects that are funded this year:
- Computer Vision:
Cordelia Schmid, Research Director at Inria, the French National Institutefor computer science and applied mathematics, and her team conduct research in computer vision, and more particularly the automatic interpretation of digital images and videos. The funded project will construct the first realistic dataset for 3D pose evaluation of humans in action. To do so, the Inria researchers will capture 3D models of humans in real-life situations while performing actions and manipulating objects using a multi-camera platform. They will then render these models in synthetic scenes simulating a moving camera. The resulting data are more realistic as they capture real hair and clothing deformations, while showing real motion blur, truncations by the image boundaries and occlusions by manipulated objects and elements of the scene.
- Machine Learning: Thorsten Joachims, Professor in the
Department of Computer Scienceand in the Department of Information Scienceat Cornell Universityin Ithaca, New York, and his team will develop new machine learning algorithms that can learn from partial information, such as user feedback in log data. Logged user interactions are one of the most ubiquitous forms of data available, as they can be recorded from a variety of systems like search engines or recommender systems at little cost. The interaction logs of such systems (e.g., a personalized newspaper) typically contain a record of the input to the system (e.g., features describing the user), the action taken by the system (e.g., presented ranking of news articles) and the feedback (e.g., clicks on the articles). When a user clicks on a search result, it often does not mean that the result is good on some absolute level, just that it is better than the higher ranked results. The proposal eliminates the need to randomize while collecting the feedback, making the learning systems more applicable to cases like search, advertising, or recommendations that may use deterministic logging.
- Robotics: With the ARA grant, the team of
Sven Koenig, professor in computer science at the University of Southern California, will study how a high number of robots find their paths efficiently and effectively in highly filled spaces for warehouse automation. The robots must be able to make good decisions in complex situations that involve a substantial degree of uncertainty, yet find solutions in a timely manner despite a large number of potential contingencies. Congested spaces exist in Amazonfulfillment centers, for example, in front of the picking stations since every robot has to cross this region, whether it delivers a shelf to a picking station or returns it to the warehouse. USC’s research intends to provide a strong foundation for building resilient algorithms for such robot systems to end collision-free paths for all robots from their respective start vertices to their respective goal vertices.
The other ARA categories are General AI, Knowledge Management and Data Quality, Machine Translation, Personalization, Search and Information Retrieval, Security, Privacy and Abuse Prevention, and Speech.
“On behalf of my team, I’d like to thank
The output of the funded projects will be made publicly available both in the form of academic publications and open source code contributions. ARA also facilitates training for students and temporary research positions for faculty. These include graduate student and post-doc internships offered by the industry partner, and visiting researcher arrangements when the scientist temporarily leaves their home university to work as an industry researcher.
The full list of winners with details on their projects is available on the ARA website: https://ara.amazon-ml.com/recipients/#2017.