Artificial Intelligence

By Bart Verheij, Marco A. Wiering. 2017

3,274 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Large Language Models

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In Large Language Models

Citations in the corpus

Counterfactuals in Explainable Artificial Intelligence (XAI): Evidence from Human Reasoning (2019, background): “Their relevance to a variety of AI applications has been known for some time, ranging from subgoal construction to the identification of planning failures, from fault diagnosis to the determination of liability [Ginsberg, 1986; Halpern and Pearl, 2005].”
Explainable AI: Beware of Inmates Running the Asylum Or: How I Learnt to Stop Worrying and Love the Social and Behavioural Sciences (2017, methodology): “For explainable AI models, these heuristics are useful from a computational perspective in large causal chains, in which causal attribution is intractable in many cases [Eiter and Lukasiewicz, 2002].”
Deep Image: Scaling up Image Recognition (2015, background): “On May 11th, 1997, IBM’s Deep Blue achieved a historic victory by defeating world chess champion Gary Kasparov in a six-game match (Campbell et al., 2002).”
My Computer Is an Honor Student - but How Intelligent Is It? Standardized Tests as a Measure of AI (2016, background): “, (Bringsjord and Schimanski, 2003; JETAI 2011; Beyer et al., 2005; Fujita et al., 2014), as they appear to require significant advances in AI technology while also being accessible, measurable, understandable, and motivating.”
The Role of Typicality in Object Classification: Improving The Generalization Capacity of Convolutional Neural Networks (2016, background): “There are some prior works on finding the right features [Blum and Langley, 1997], choosing the appropriate train set and how to order training examples for learning better classifiers [Bengio et al.”
Artificial intelligence in healthcare: transforming the practice of medicine (2021, background): “9 AI systems have the potential to anticipate problems or deal with issues as they come up and, as such, operate in an intentional, intelligent and adaptive manner.”
Connecting the Dots in Trustworthy Artificial Intelligence: From AI Principles, Ethics, and Key Requirements to Responsible AI Systems and Regulation (2023, background): “It is also important to be answerable to those who are affected by the technology [10], for example in the case of a suicide the company may need to be answerable to the family of the victim.”
Anthropomorphism in AI (2020, background): “AI is thus often described as systems that act like humans, that is machines performing functions requiring intelligence if performed by humans (Kurzweil 1990), and AI development is understood as the attempt to make computers do things that no humans do better (Rich and Knight 1991).”
Libratus: The Superhuman AI for No-Limit Poker (2017, background): “AIs have beaten top humans in chess [Campbell et al., 2002] and Go [Silver et al., 2016].”
Classical Planning in Deep Latent Space: Bridging the Subsymbolic-Symbolic Boundary (2017, methodology): “In combinatorial search, FFNs have been used as heuristic functions for the sliding-tile puzzle and Rubik’s Cube [Arfaee et al., 2011].”