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Found 7 papers, 0 papers with code
Artificial Intelligence and Life in 2030: The One Hundred Year Study on Artificial Intelligence
In September 2016, Stanford's "One Hundred Year Study on Artificial Intelligence" project (AI100) issued the first report of its planned long-term periodic assessment of artificial intelligence (AI) and its impact on society.
Mapping DNN Embedding Manifolds for Network Generalization Prediction
Understanding Deep Neural Network (DNN) performance in changing conditions is essential for deploying DNNs in safety critical applications with unconstrained environments, e. g., perception for self-driving vehicles or medical image analysis.
Network Generalization Prediction for Safety Critical Tasks in Novel Operating Domains
We propose the task Network Generalization Prediction: predicting the expected network performance in novel operating domains.
A New Age of Computing and the Brain
In December 2014, a two-day workshop supported by the Computing Community Consortium (CCC) and the National Science Foundation's Computer and Information Science and Engineering Directorate (NSF CISE) was convened in Washington, DC, with the goal of bringing together computer scientists and brain researchers to explore these new opportunities and connections, and develop a new, modern dialogue between the two research communities.
Dependable Neural Networks for Safety Critical Tasks
Our results demonstrate that we can accurately predict the ML Dependability, Task Undependability, and Harmful Undependability for operating conditions that are significantly different from the testing conditions.
Information and Multi-Sensor Coordination
We consider the sensors of a multi-sensor system to be members or agents of a team, able to offer opinions and bargain in group decisions.
Estimation Procedures for Robust Sensor Control
In this paper, we evaluate three estimation techniques: the extended Kalman filter, a discrete Bayes approximation, and an iterative Bayes approximation.
