What is the difference between Artificial Intelligence and Machine Learning? My students often ask the question: “What is the difference between Artificial Intelligence (AI) and Machine Learning (ML) – and is deep learning (DL) belonging to either AI or ML?”. In the following I provide a I) brief answer, a II) formal short answer and […]
Author Archive for: Andreas Holzinger
About Andreas Holzinger
Andreas and his Group work consistently on a synergistic combination of methodologies and approaches of two areas that offer ideal conditions towards unraveling problems in sensemaking of high-dimensional, complex data sets: Human-Computer Interaction (HCI) and Knowledge Discovery/Data Mining (KDD), with the goal of supporting human intelligence with machine learning – human-in-the-loop – to discover new, previously unknown insights into the data.
Entries by Andreas Holzinger
26.10.2017. Today, Prof. Kurt Zatloukal and his group together with the digital pathology team of 3DHISTECH, our industrial partner, completed the installation of the new generation panoramic P1000 scanner. The world’s fastest whole slide image scanner (WSI) is now located in Graz. The current scanner outperforms current state-of-the-art systems by a factor 6, which provides enormous […]
A huge motivation for us in continuing to study interactive Machine Learning (iML)  – with a human in the loop  (see our project page) is that modern deep learning models are often considered to be “black-boxes” . A further drawback is that such models have no explicit declarative knowledge representation, hence have difficulty […]
The CD-MAKE 2017 in the context of the ARES conference series was a full success in beautiful Reggio di Calabria.
In machine learning deep convolutional networks (deep learning) are very successful for solving particular problems  – at least when having many training samples. Great success has been made recently, e.g. in automatic game playing by AlphaGo (see the nature news here). As fantastic these approaches are, it should be mentioned that deep learning has […]
Inaugural Editorial Paper published: Holzinger, A. 2017. Introduction to Machine Learning & Knowledge Extraction (MAKE). Machine Learning and Knowledge Extraction, 1, (1), 1-20, doi:10.3390/make1010001. http://www.mdpi.com/2504-4990/1/1/1 Machine Learning and Knowledge Extraction (MAKE) is an inter-disciplinary, cross-domain, peer-reviewed, scholarly open access journal to provide a platform to support the international machine learning community. It publishes original research […]
The Journal BMC Medical Informatics and Decision Making (SCI IF (2015): 2,042) invites to submit to a new thematic series on open data for discovery science https://bmcmedinformdecismak.biomedcentral.com/articles/collections/odds Note: Excellent submissions to the IFIP Cross Domain Conference on Machine Learning and Knowledge Discovery (CD-MAKE), (Submission due to May, 15, 2017) relevant to the topics described below, […]
The Google Research Group  is always doing awesome stuff, the most recent one is on Federated Learning , which enables e.g. smart phones (of course any computational device, and maybe later all internet-of-things, intelligent sensors in either smart hospitals or in smart factories etc.) to collaboratively learn a shared representation model, whilst keeping all […]
Machine learning is the fastest growing field in computer science, and Health Informatics is amongst the greatest application challenges, providing benefits in improved medical diagnoses, disease analyses, and pharmaceutical development – towards future precision medicine. Talk announcement: Friday, 12th May, 2017, 10:00, Seminaraum 137, Parterre, Inffeldgasse 16c Integrated interactomes and pathways in precision medicine by […]
Many services of our every day life rely meanwhile on machine learning. Machine learning is a very practical field and provides powerful technologies that allows machines (i.e. computers) to learn from prior data, to extract knowledge, to generalize and to make predictions – similar as we humans can do (see the MAKE intro). There is […]