Entries by Andreas Holzinger

Holzinger Group at NIPS

Our crazy iML-Concept has been accepted at the CiML 2016 workshop (organized by Isabelle Guyon, Evelyne Viegas, Sergio Escalera, Ben Hammer & Balazs Kegl) at NIPS 2016 (December, 5-10, 2016)  in Barcelona: https://docs.google.com/viewer?a=v&pid=sites&srcid=Y2hhbGVhcm4ub3JnfHdvcmtzaG9wfGd4OjFiMGRmNzQ5MmM5MTZhYzE

Obama on humans-in-the-loop

How artificial intelligence will affect jobs In an discussion with Barack OBAMA [1] on how artificial intelligence will affect jobs, he emphasized how important human-in-the-loop machine learning will become in the future. Trust, transparency and explainabiltity will be THE driving factors of future AI solutions! The discussion interview was led by the Wired [2] Editor […]

Google releases their Syntactic Parser Open Source

Google researchers spend a lot of time thinking about how computer systems can read and understand human language in order to process it in intelligent ways. On May, 12, 2016 Slav Petrov (expertise) based in New York and leading the machine learning for natural language group (Slav Petrov Page), announced that they released SyntaxNet as […]

Deep Learning Playground openly available

TensorFlow – part of the Google brain project – has recently open sourced on GitHub a nice playground for testing and learning the behaviour of deep learning networks, which also can be used following the Apache Licence: http://playground.tensorflow.org Background: TensorFlow is an open source software library for machine learning. There is a nice video “large […]


Interactive machine learning for health informatics: when do we need the human-in-the-loop? Machine learning (ML) is the fastest growing field in computer science, and health informatics is among the greatest challenges. The goal of ML is to develop algorithms which can learn and improve over time and can be used for predictions. Most ML researchers […]

Papers due to April, 30, 2016: Privacy Aware Machine Learning (PAML) for Health Data Science

We are organizing a special session on Privacy Aware Machine Learning for Health Data Science at the 11th international Conference on Availability, Reliability and Security (ARES and CD-ARES), Salzburg, Austria, August 29 – September, 2, 2016 supported by the International Federation of Information Processing IFIP >  TC5 and WG 8.4 and WG 8.9 http://cd-ares-conference.eu http://www.ares-conference.eu […]

Yahoo Labs released largest-ever annonymized machine learning data set for researchers

In January 2016, Yahoo announce the public release of the largest-ever machine learning data set to the international research community. The data set stands at a massive ~110B events (13.5TB uncompressed) of anonymized user-news item interaction data, collected by recording the user-news item interactions of about 20M users from February 2015 to May 2015. see: […]

January, 27, 2016, Major breakthrough in AI research …

Mastering the game of Go with deep neural networks and tree search – a very recent paper in Nature: Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., Van Den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., Dieleman, S., Grewe, D., Nham, J., Kalchbrenner, N., Sutskever, I., Lillicrap, T., Leach, […]

January, 26, 2016, Workshop “Machine Learning for Biomedicine” TU Graz

Date: Tuesday, 26th January 2016, Start: 10:00, End: 17:00; Venue: Graz University of Technology, Institute of Computer Graphics and Knowledge Visualization CGV, hosted by Prof. Tobias SCHRECK Address: Inffeldgasse 16c, A-8010 Graz <maps and directions> Machine learning is the most growing field in computer science  [Jordan, M. I. & Mitchell, T. M. 2015. Machine learning: […]