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Trends in Security Information
The HSD Trendmonitor is designed to provide access to relevant content on various subjects in the safety and security domain, to identify relevant developments and to connect knowledge and organisations. The safety and security domain encompasses a vast number of subjects. Four relevant taxonomies (type of threat or opportunity, victim, source of threat and domain of application) have been constructed in order to visualize all of these subjects. The taxonomies and related category descriptions have been carefully composed according to other taxonomies, European and international standards and our own expertise.
In order to identify safety and security related trends, relevant reports and HSD news articles are continuously scanned, analysed and classified by hand according to the four taxonomies. This results in a wide array of observations, which we call ‘Trend Snippets’. Multiple Trend Snippets combined can provide insights into safety and security trends. The size of the circles shows the relative weight of the topic, the filters can be used to further select the most relevant content for you. If you have an addition, question or remark, drop us a line at info@securitydelta.nl.
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Today’s AI is driven by Machine learning
Algorithm approachesToday’s AI is driven by Machine learning81Fumo, D. (2017). Types of machine learning algorithms you should know. Retrieved from: https://towardsdatascience.com/. The frontiers of AI research that use these novel types of algorithms are described in the Appendix.Traditional algorithms are described as Rule-Based. Systems based on these algorithms get input and follow a set of pre-definedrules and instructions to generate output. The current uptake in AI is largely due to the application of Machine Learning1algorithms, whose performance change by exposing them to more data over time. These algorithms make use of (dynamic) input in order to derive machine-made patterns from the information and translate these to insights and actions.Types of Machine Learning-Based AI:•Supervised Learning: Labelled data by a human is put through an algorithm that models the relationships between each label and the input values.•Unsupervised Learning: Unlabelled data is put through an algorithm that identifies rules, detects patterns, and summarizes and groups data points to derive insights.•Reinforcement Learning: An autonomous, self-teaching system learns by trial and error to achieve the best outcomes. It performs actions aiming to maximize rewards.Deep Learning: highly complex subset of Machine Learning that uses algorithms that mimic the neural network of the brain, to progressively extract higher level patterns and learn from vast amounts of (un)labelled data. Recently developments in Deep Learning have outperformed humans and classical computers at achieving several goals, such as winning complex games (GO, Starcraft), text translation (Google Translate) and classifying radiology images.