iML with the human-in-the-loop mentioned among 10 coolest applications of machine learning

Within the “Two Minute Papers” series, Karol Károly Zsolnai-Fehér from the Institute of Computer Graphics and Algorithms at the Vienna University of Technology mentions among “10 even cooler Deep Learning Applications” our human-in-the-loop paper:

Seid Muhie Yimam, Chris Biemann, Ljiljana Majnaric, Šefket Šabanović & Andreas Holzinger 2016. An adaptive annotation approach for biomedical entity and relation recognition. Springer/Nature: Brain Informatics, 3, (3), 157-168, doi:10.1007/s40708-016-0036-4

Watch the video here (iML is mentinoned from approx. 1:20):

Here the list of all 10 papers discussed within this 2-minutes-video

1. Geolocation –
2. Super-resolution –
3. Neural Network visualizer –…
4. Recurrent neural network for sentence completion:
5. Human-in-the-loop and Doctor-in-the-loop:
6. Emoji suggestions for images –
7. MNIST handwritten numbers in HD –
8. Deep Learning solution to the Netflix prize –
9. Curating works of art –
10. More robust neural networks against adversarial examples –…
The Keras library:

A) The basic principle of the iML human-in-the-loop approach:

Andreas Holzinger 2016. Interactive Machine Learning for Health Informatics: When do we need the human-in-the-loop? Brain Informatics, 3, (2), 119-131, doi:10.1007/s40708-016-0042-6

B) The entry in the GI Lexikon:

C) The experimental proof-of-concept:

Andreas Holzinger, Markus Plass, Katharina Holzinger, Gloria Cerasela Crisan, Camelia-M. Pintea & Vasile Palade 2017. A glass-box interactive machine learning approach for solving NP-hard problems with the human-in-the-loop. arXiv:1708.01104.

D) Outline and Survey of application possibilities:

Andreas Holzinger, Chris Biemann, Constantinos S. Pattichis & Douglas B. Kell 2017. What do we need to build explainable AI systems for the medical domain? arXiv:1712.09923.

Andreas Holzinger, Bernd Malle, Peter Kieseberg, Peter M. Roth, Heimo Müller, Robert Reihs & Kurt Zatloukal 2017. Towards the Augmented Pathologist: Challenges of Explainable-AI in Digital Pathology. arXiv:1712.06657.