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 – https://arxiv.org/abs/1602.05314
2. Super-resolution – https://arxiv.org/pdf/1511.04491v1.pdf
3. Neural Network visualizer – https://experiments.mostafa.io/public/…
4. Recurrent neural network for sentence completion:
5. Human-in-the-loop and Doctor-in-the-loop: https://link.springer.com/article/10.1007/s40708-016-0036-4
6. Emoji suggestions for images – https://emojini.curalate.com/
7. MNIST handwritten numbers in HD – https://blog.otoro.net/2016/04/01/generating-large-images-from-latent-vectors
8. Deep Learning solution to the Netflix prize – https://karthkk.wordpress.com/2016/03/22/deep-learning-solution-for-netflix-prize/
9. Curating works of art –
10. More robust neural networks against adversarial examples – https://cs231n.stanford.edu/reports201…
The Keras library: https://keras.io/
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:
https://gi.de/informatiklexikon/interactive-machine-learning-iml
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.
