Entries by Andreas Holzinger

Google Brain says we urgently need a Research Framework around the field of interpretability

In a recent interview Been KIM from the Google Brain team emphasizes the signficance of research in explainable AI, the importance of Human-Computer Interaction (HCI) for Artificial Intelligence generally and Machine Learning specifically (see the differences between AI and ML here), and the urgent need of an research framework around the field of interpretability. Listen […]

“Machine Learning for Health Informatics” Lecture Notes in Artificial Intelligence 9605 > 40,626 downloads 2017

Since its online publication on December 10, 2016 the Volume edited by Andreas Holzinger “Machine Learning for Health Informatics” Springer Lecture Notes in Artificial Intelligence LNAI Volume 9605, has been downloaded 54,960 times as of today (May, 11, 2018, 20:00 CEST) and 44,988 with status as of April 2018 according to the official Springer Bookmetrix […]

AI will change Radiology – NOT replace Radiologists

After the rather shocking statement of Geoffrey HINTON during the Machine Learning and Market for Intelligence Conference in Toronto, where he recommended that hospitals should stop training radiologists, because deep learning will replace them (watch video below), on March, 27, 2018 Thomas H. DAVENPORT and Keith J. DREYER published a really nice article on “AI […]

Human-in-the-loop AI

Human-in-the-Loop-AI This is really very interesting. In the recent April, 5, 2018, TWiML & AI (This Week in Machine Learning and Artificial Intelligence) podcast, Robert MUNRO (a graduate from Stanford University, who is an recognized expert in combining human and machine intelligence) reports on the newly branded Figure Eight [1] company, formerly known as CrowdFlower. […]

A good proof of the importance of the HCI-KDD approach, worth: 2,1 Billion USD !

Our strategic aim is to find solutions for data intensive problems by the combination of two areas, which bring ideal pre-conditions towards understanding intelligence and to bring business value in AI: Human-Computer Interaction (HCI) and Knowledge Discovery (KDD). HCI deals with questions of human intelligence, whereas KDD deals with questions of artificial intelligence, in particular […]

On-Device Machine Intelligence

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. […]

Prefetching – Predicting what will be most likely needed next

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 […]

Python in Machine Learning still Nr. 1 and increasing

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. […]

iML with the human-in-the-loop mentioned among 10 coolest applications of machine learning

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. […]