Entries by Andreas Holzinger

Call for Papers – Privacy Aware Machine Learning PAML due to April, 1, 2017

Privacy Aware Machine Learning (PAML) for Health Data Science Special Session on September, 1, 2017, organized by Andreas HOLZINGER, Peter KIESEBERG, Edgar WEIPPL and A Min TJOA in the context of the 12th International Conference on Availability, Reliability and Security (ARES and CD-ARES), Reggio di Calabria, Italy, August 29 – September, 2, 2017 Session Homepage […]

3,2 Trillion USD on health per year

The U.S. spends more on health care than any other country Dieleman et al. (2016) just (Dec, 27, 2016) published a paper [1] which discusses data from the National Health Expenditure Accounts to estimate US spending on personal health care and public health, according to condition, age and sex group, and type of care. This […]

LNAI 9605 Machine Learning for Health Informatics available

NEW – just appeared – NEW Holzinger, A. (ed.) 2016. Machine Learning for Health Informatics: State-of-the-Art and Future Challenges. Cham: Springer International Publishing, doi:10.1007/978-3-319-50478-0 [book homepage] Machine learning (ML) is the fastest growing field in computer science, and Health Informatics (HI) is amongst the greatest application challenges, providing future benefits in improved medical diagnoses, disease […]

NIPS 2016 is over

A crazy 5700-people event is over: NIPS 2016 in Barcelona. Registration on Sunday, 4th December, on Monday, 5th traditionally the tutorials were presented concluded by the first keynote talk given by Yann LeCun (now director at Facebook AI research) and completed by the official opening and the first poster presentation.  On Tuesday, Dec 6th, after […]

Machine Learning with Fun

Google Research hosts a number of very interesting so-called A.I. experiments, which let you play with machine learning algorithms in a very amusing way, e.g. Quick Draw, where a neural network learns to recognize hand drawn sketches (called doodles), see: https://quickdraw.withgoogle.com which is part of the A.I. Experiments platform: https://aiexperiments.withgoogle.com and here the explanatory video: […]

Visualization of High Dimensional Data

Google is doing experiments with visualization of high dimenisonal data. This experiment helps visualize what’s happening in machine learning. It allows coders to see and explore their high-dimensional data. The goal is to eventually make this an open-source tool within TensorFlow, so that any coder can use these visualization techniques to explore their data. Built […]

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 on how artificial intelligence will affect jobs, by President Barack OBAMA, the Wired Editor Scott DADICH, and MIT Media Lab Director Joi ITO,  the president demonstrates good understanding of the field and indicates the importance of the humans-in-the-loop, despite all progress of fully automatic approaches.   […]

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 […]