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Artificial intelligence in mental health is the application of artificial intelligence (AI), computational technologies and algorithms to supplement the understanding, diagnosis, and treatment of mental health disorders. [1] [2] [3] AI is becoming a ubiquitous force in everyday life which can be seen through frequent operation of models like ...
Marvin Minsky et al. raised the issue that AI can function as a form of surveillance, with the biases inherent in surveillance, suggesting HI (Humanistic Intelligence) as a way to create a more fair and balanced "human-in-the-loop" AI. [61] Explainable AI has been recently a new topic researched amongst the context of modern deep learning.
Artificial intelligence in pharmacy is the application of artificial intelligence (AI) [118] [119] [120] to the discovery, development, and the treatment of patients with medications. [121] AI in pharmacy practices has the potential to revolutionize all aspects of pharmaceutical research as well as to improve the clinical application of ...
Soar [1] is a cognitive architecture, [2] originally created by John Laird, Allen Newell, and Paul Rosenbloom at Carnegie Mellon University.. The goal of the Soar project is to develop the fixed computational building blocks necessary for general intelligent agents – agents that can perform a wide range of tasks and encode, use, and learn all types of knowledge to realize the full range of ...
AI also helps improve the healthcare experience by using an app to identify patients' anxieties. In medical research, AI helps to analyze and evaluate the patterns and complex data. For instance, AI is important in drug discovery because it can search relevant studies and analyze different kinds of data.
This has led to advocacy and in some jurisdictions legal requirements for explainable artificial intelligence. [69] Explainable artificial intelligence encompasses both explainability and interpretability, with explainability relating to summarizing neural network behavior and building user confidence, while interpretability is defined as the ...
Knowledge-representation is a field of artificial intelligence that focuses on designing computer representations that capture information about the world that can be used for solving complex problems. The justification for knowledge representation is that conventional procedural code is not the best formalism to use to solve complex problems.
Open-source artificial intelligence is an AI system that is freely available to use, study, modify, and share. [1] These attributes extend to each of the system's components, including datasets, code, and model parameters, promoting a collaborative and transparent approach to AI development. [1]