Introduction to MAchine Learning & Knowledge Extraction (MAKE)

By Andreas Holzinger. 2017

73 citations recorded by the source corpus.

The grand goal of Machine Learning is to develop software which can learn from previous experience—similar to how we humans do. Ultimately, to reach a level of usable intelligence, we need (1) to learn from prior data, (2) to extract knowledge, (3) to generalize—i.e., guessing where probability function mass/density concentrates, (4) to fight the curse of dimensionality, and (5) to disentangle underlying explanatory factors of the data—i.e., to make sense of the data in the context of an application domain. To address these challenges and to ensure successful machine learning applications in various domains an integrated machine learning approach is important. This requires a concerted international effort without boundaries, supporting collaborative, cross-domain, interdisciplinary and transdisciplinary work of experts from seven sections, ranging from data pre-processing to data visualization, i.e., to map results found in arbitrarily high dimensional spaces into the lower dimensions to make it accessible, usable and useful to the end user. An integrated machine learning approach needs also to consider issues of privacy, data protection, safety, security, user acceptance and social implications. This paper is the inaugural introduction to the new journal of MAchine Learning & Knowledge Extraction (MAKE). The goal is to provide an incomplete, personally biased, but consistent introduction into the concepts of MAKE and a brief overview of some selected topics to stimulate future research in the international research community.

Subfields: Artificial Intelligence

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Citations in the corpus

What do we need to build explainable AI systems for the medical domain? (2017, background): “…fosters integrative ML, i.e. a synergistic combination of diverse methodological approaches in a concerted eﬀort to augment human intelligence with artiﬁcial intelligence, and eventually to enable what neither of them could do on their own (Holzinger, 2012, 2013, 2017; Holzinger et al., 2017a).”
Causability and explainability of artificial intelligence in medicine (2019, background): “The grand challenges are in sense-making, in context understanding, and in decision making under uncertainty (Holzinger, 2017).”
Towards the Augmented Pathologist: Challenges of Explainable-AI in Digital Pathology (2017, background): “ML is a very practical ﬁeld and applies the ﬁndings of AI research to the design, development and testing of algorithms that can learn from data, gain knowledge from experience and improve their learning behavior over time (Holzinger, 2017).”
Machine learning enhanced virtual autopsy (2017, background): “2017;7(4):3-7 clinical death to avoid the risk that ultimately the algorithms will fail to recognize autolysis, which is the most common phenomenon in these cases.”