Artificial intelligence has once again become a topic of public interest. Predominantely, this is due to the recent advances in Deep Learning, with AlphaGo just being one example. This was made possible in part by bigger data sets and more computational resources. Currently, researchers worldwide are working hard to improve Deep Learning algorithms, apply them on various scenarios and build better systems to make use of such algorithms.
The levels of autonomy that these systems aim for varies with the application scenario and also with the capabilities of each system: from assistive systems that interact with the human to reach decisions, to fully autonomous systems. For instance, a strong focus concentrated on interpreting sensory input, such as, autonomous classification of images, using images to decide the next move in a game. These scenarios open the field for autonomous systems that drive robots, cars, drones, etc. On the other hand, an increasingly important task is to assist the human in interpreting large amounts of data available. As the volumes of data pushes the limits of exploratory data analysis, the role of personal assistants, offering intelligent processing and advice, becomes increasingly important. Intelligent assistance is also sought in tasks were ultimately autonomous system is desired, but cannot be realized currently, for example ADAS (Advanced Driving Assistance Systems) help detect lane changes or braking distance from other cars.
The DL-AAS workshop aims to support the scientific progress by bridging multiple application areas, where Deep Learning has been applied successfully. These three focus areas are: Natural Language Processing, Manufacturing and Autonomous Driving. The workshop strives to foster a vivid knowledge exchange between these very different fields and thus serve as a source for inspirations. Therefore we welcome contributions that allow for such a lively discussion.
Overall topics to be addressed by this workshop include but are not limited to:
Feel free to pass this information to anyone that would find this of interest.
Researchers and practitioners from the field related to deep learning, e.g. autonomous driving, manufacturing and natural language processing.
We welcome submissions in the ACM format (templates are available here). The papers are expected to be at least 5 pages and up to 12 pages and need to contain a copyright statement like: "Copyright held by the author(s)." .
Submissions are conducted via: https://easychair.org/conferences/?conf=iknow2017
Each presentation is expected to last approximately 20 minutes to allow for 10 minutes of discussion.
The workshop takes place within the i-KNOW 2017 conferenceMesse Congress Graz