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Interactive Deep Learning Enterprise

Interactive Deep Learning Enterprise

In recent years, machines have surpassed humans in the performance of specific and narrow tasks such as some aspects of image recognition or decision making along clinical pathways in the medical domain (weak AI). Although it is very unlikely that machines will exhibit broadly applicable intelligence comparable to or exceeding that of humans in the next 30 years (strong AI), it is to be expected that machines will reach and exceed human performance on more and more applied tasks. To develop the positive aspects of AI, manage its risks and challenges, and ensure that everyone has the opportunity to help in building an AI-enhanced society and to participate in its benefits, in this project, human intelligence and machine learning (ML) take the center stage: Interactive Machine Learning (IML) is the design and implementation of algorithms and intelligent user interface frameworks that facilitate ML with the help of human interaction. Our goal is to improve human-machine interaction using state-of-the-art Human-Computer Interaction (HCI) approaches, as well as systems built on state-of-the-art ML techniques. In this project, we focus on Interactive Deep Learning (IDL): deep learning (DL) approaches for IML. We want computers to learn from humans by, for example, interacting with them in natural language and observing them. Our goal in No-IDLE is to improve human-machine interaction to improve DL models using both state-of-the-art approaches to human-computer interaction and DL techniques. Basic research in this corridor project will also provide deeper insights into user behaviors, needs, and goals. Machine learning and DL should become accessible to millions of end users. In addition, we emphasize the role of multimodal interaction (MMI) and mixed-initiative interaction. The key challenge in No-IDLE is to develop a methodology for IDL, which will be of great importance as we interact more with semi-intelligent machines. In addition, it is critical that IML is well understood and defined. In No-IDLE, we study IDL from four different perspectives: HCI, ML, NLP, MMI. No-IDLE is a basic research project, with the goal of improving our understanding of IML. We expect technical and scientific results through the collaboration of the four IML working groups on a specific application around IML: the interactive creation of photo books.


BMBF - Federal Ministry of Education, Science, Research and Technology


BMBF - Federal Ministry of Education, Science, Research and Technology

Publications about the project

Aliki Anagnostopoulou; Mareike Hartmann; Daniel Sonntag

In: Proceedings of The Fourth Workshop on Simple and Efficient Natural Language Processing (SustaiNLP). ACL Workshop on Simple and Efficient Natural Language Processing (SustaiNLP-2023), located at Annual Meeting of the Association for Computational Linguistics 2023, July 13, Toronto, Canada, Association for Computational Linguistics, 7/2023.

To the publication