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The Next Big Thing(s) in Unsupervised Machine Learning: Five Lessons from Infant Learning
After a surge in popularity of supervised Deep Learning, the desire to reduce the dependence on curated, labelled data sets and to leverage the vast quantities of unlabelled data available recently triggered renewed interest in unsupervised learning algorithms.
Trepan Reloaded: A Knowledge-driven Approach to Explaining Artificial Neural Networks
Whilst a plethora of approaches have been developed for post-hoc explainability, only a few focus on how to use domain knowledge, and how this influences the understandability of global explanations from the users' perspective.
The What, the Why, and the How of Artificial Explanations in Automated Decision-Making
The increasing incorporation of Artificial Intelligence in the form of automated systems into decision-making procedures highlights not only the importance of decision theory for automated systems but also the need for these decision procedures to be explainable to the people involved in them.
Neural-Symbolic Learning and Reasoning: A Survey and Interpretation
Recent studies in cognitive science, artificial intelligence, and psychology have produced a number of cognitive models of reasoning, learning, and language that are underpinned by computation.
What Does Explainable AI Really Mean? A New Conceptualization of Perspectives
We characterize three notions of explainable AI that cut across research fields: opaque systems that offer no insight into its algo- rithmic mechanisms; interpretable systems where users can mathemat- ically analyze its algorithmic mechanisms; and comprehensible systems that emit symbols enabling user-driven explanations of how a conclusion is reached.
Reasoning in Non-Probabilistic Uncertainty: Logic Programming and Neural-Symbolic Computing as Examples
This article aims to achieve two goals: to show that probability is not the only way of dealing with uncertainty (and even more, that there are kinds of uncertainty which are for principled reasons not addressable with probabilistic means); and to provide evidence that logic-based methods can well support reasoning with uncertainty.
Efficient Dodgson-Score Calculation Using Heuristics and Parallel Computing
Conflict of interest is the permanent companion of any population of agents (computational or biological).
