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

14.12.2016 LNAI 9605 just appeared Machine Learning for Health Informatics Lecture Notes in Artificial Intelligence LNAI 9605 Holzinger, Andreas (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 […]

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. There you can play with machine learning algorithms in a very amusing way. A recent example is QUICK, DRAW *). This is an online guessing game that challenges humans to hand sketch (called doodles) a given object. The game uses a  neural network to […]

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 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 […]