One very interesting approach of federated machine learning is presented by Sujith Ravi from Google: Machine learning models (e.g. CNN) are sucessfully used for the design of intelligent systems capable of visual recognition, speech and language understanding. Most of these are running on a cloud – which is often inpredictable where it is physically running. […]
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
A very interesting paper has just been published about prefetching, which is a nice machine learning solution: predicting which information will be most likely useful next and consequently can be prepared in advance: Milad Hashemi, Kevin Swersky, Jamie A Smith, Grant Ayers, Heiner Litz, Jichuan Chang, Christos Kozyrakis & Parthasarathy Ranganathan 2018. Learning Memory Access […]
There is of course no such thing like a ‘best language for machine learning’ – but as a matter of fact Python is still Nr. 1 and increasing: Image Source: https://stackoverflow.blog/2017/09/06/incredible-growth-python/ We use in all our courses Python due to the fact that it is an “industrial standard” and widely available. I would love e.g. […]
Within the “Two Minute Papers” series, Karol Károly Zsolnai-Fehér from the Institute of Computer Graphics and Algorithms at the Vienna University of Technology mentions among “10 even cooler Deep Learning Applications” our human-in-the-loop paper: Seid Muhie Yimam, Chris Biemann, Ljiljana Majnaric, Šefket Šabanović & Andreas Holzinger 2016. An adaptive annotation approach for biomedical entity and relation recognition. […]
Congratulations to Arthur GRETTON from the Gatsby Computational Neuroscience Unit at the University College London an his team. Their paper titled “A Linear-Time Kernel Goodness-of-Fit Test” authored by Wittawat JITKRITTUM, Wenkai XU, Zoltan SZABO, Kenji FUKUMIZU and Arthur GRETTON won the prestigous NIPS 2017 best paper award. In the interview by Sam Charringtion from TWiML&AI, […]
We experience enormous advances in AI and ML (see here for the difference), with impressive, daily visible improvements in technical performance, particularly in speech recognition, deep learning from images, autonomous driving, etc. It is really great that the Google Brain team led by Jeff Dean and the Google Initiative People and Artificial Intelligence Research (PAIR) […]
What is the difference between Artificial Intelligence and Machine Learning? My students repeatedly 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 […]
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.