What is machine learning?

Many services of our every day life rely meanwhile on machine learning – a field of science and a powerful technology that allows machines to learn from data; a very nice info graphic by the Royal Society – interactive with a quiz – can be found here:

Royal Society Infographic “What is machine learning?”

This is part of a info campaign about machine learning from the Royal Society:

https://royalsociety.org/topics-policy/projects/machine-learning/

The Royal Society was formed by a group of natural scientists influenced by Francis Bacon (1561-1626).  The first ‘learned society’ meeting on 28 November 1660 followed a lecture at Gresham College by Christopher Wren. Joined by Robert Boyle and John Wilkins and others, the group received royal approval by King Charles II (1630-1685) in 1663 and was known since as ‘The Royal Society of London for Improving Natural Knowledge’.

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 scale deep learning” by Jeffrey Dean.  TensorFlow is  an interface for expressing machine learning algorithms along with an implementation for executing such algorithms on a variety of heterogeneous systems, ranging from smartphones to high-end computer clusters and  grids of thousands of computational devices (e.g. GPU). The system has been used for research in various areas of computer science (e.g. speech recognition, computer vision, robotics, information retrieval, natural language processing, geographic information extraction, computational drug discovery). The TensorFlow API and a reference implementation were released as an open-source package under the Apache 2.0 license on 9th November 2015 and is available at www.tensorflow.org

Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J. & Devin, M. 2016. TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems. arXiv preprint arXiv:1603.04467.

It is also discussed on episode 24 of talking machines.

 

 

Happy Scientific 2016

We wish you a prosperous scientific 2016 with a lot of crazy ideas and successful breakthrough discoveries !

Happy New 2016

Happy New Year from the Holzinger Group HCI-KDD

Merry Christmas and a Happy 2015 from the Holzinger Group

Merry Christmas and a Happy 2015 from the Holzinger Group

Merry Christmas and a Happy 2015 from the Holzinger Group

hci4all.at is now hci-kdd.org

Our website appears in a new light!

We welcome our visitors to our new hci-kdd.org website. In order to keep pace with time, our website is now responsive with a fresh design.

HCI4ALL is now HCI-KDD

Our digital world, full of mobile computing devices and ubiquitous sensor networks produce increasingly large, complex, high-dimensional and weakly structured data sets, and increasing volumes of unstructured information. These enormous amounts of data require novel, efficient and interactive solutions for knowledge discovery/data mining.  A synergistic combination of methodologies and approaches of two areas offer ideal conditions towards unraveling such problems: Human-Computer Interaction (HCI) and Knowledge Discovery/Data Mining (KDD), with the goal of supporting human intelligence with machine learning, putting the “human-in-the-loop “. Our goal is to interactively discover new, previously unknown insights into the data and we are passionate on extending advanced methods including time (e.g. information entropy) and space (e.g. computational topology), along with user-centered software engineering methods to create interactive software for mobile applications & content analytics techniques.

Science is to test crazy ideas – Engineering is to put these ideas into Business!