Search results
Results from the WOW.Com Content Network
The use of explainable artificial intelligence (XAI) in pain research, specifically in understanding the role of electrodermal activity for automated pain recognition: hand-crafted features and deep learning models in pain recognition, highlighting the insights that simple hand-crafted features can yield comparative performances to deep ...
First, regression analysis is widely used for prediction and forecasting, where its use has substantial overlap with the field of machine learning. Second, in some situations regression analysis can be used to infer causal relationships between the independent and dependent variables. Importantly, regressions by themselves only reveal ...
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.
Jerry M. Mendel is an engineer, academic, and author.He is professor emeritus of Electrical and Computer Engineering at the University of Southern California. [1]Mendel has authored and co-authored 600 technical papers and 13 books including Uncertain Rule-based Fuzzy Logic Systems: Introduction and New Directions, Explainable Uncertain Rule-Based Fuzzy Systems, Perceptual Computing: Aiding ...
Xai, XAI or xAI may refer to: Explainable artificial intelligence, in artificial intelligence technology; Xai-Xai, a city in the south of Mozambique; XAI, the IATA airport code for Xinyang Minggang Airport, in Xinyang, China; xai, the ISO 639-3 language code of Kaimbé language, an extinct language in Brazil.
Elon Musk is reportedly looking to raise up to $6 billion for xAI, his nascent ChatGPT competitor, according to a Financial Times report dropped on Friday morning. Though by afternoon, Musk had ...
DARPA established the XAI ("Explainable Artificial Intelligence") program in 2014 to try to solve these problems. [260] Several approaches aim to address the transparency problem. SHAP enables to visualise the contribution of each feature to the output. [261] LIME can locally approximate a model's outputs with a simpler, interpretable model. [262]
Extreme learning machines are feedforward neural networks for classification, regression, clustering, sparse approximation, compression and feature learning with a single layer or multiple layers of hidden nodes, where the parameters of hidden nodes (not just the weights connecting inputs to hidden nodes) need to be tuned.