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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.
Research is also underway to develop a tool combining explainable AI and deep learning to prescribe personalized treatment plans for children with schizophrenia. [ 21 ] In January of 2024, Cedars-Sinai physician-scientists developed a first-of-its-kind program that uses immersive virtual reality and generative artificial intelligence to provide ...
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 ...
A pioneer in the use of artificial intelligence in healthcare was American biomedical informatician Edward H. Shortliffe. This field deals with utilization of machine-learning algorithms and artificial intelligence, to emulate human cognition in the analysis, interpretation, and comprehension of complicated medical and healthcare data.
As the founder and general chair of the World Conference on Explainable artificial intelligence [2], he performs fundamental research in the area of computational models of Cognitive Load and is the editor of books and journals with Springer Publishing [3] and Frontiers Media [4].
Similarly to the reward model, the human feedback policy is also fine-tuned over the pre-trained model. The objective of this fine-tuning step is to adapt the pre-existing, unaligned model (initially trained in a supervised manner) to better align with human preferences by adjusting its parameters based on the rewards derived from human feedback.
This has led to advocacy and in some jurisdictions legal requirements for explainable artificial intelligence. [68] 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 ...
Artificial intelligence engineering (AI engineering) is a technical discipline that focuses on the design, development, and deployment of AI systems. AI engineering involves applying engineering principles and methodologies to create scalable, efficient, and reliable AI-based solutions.
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