Edge Computing, Embedded Systems
A blueprint for creating algorithms that more effectively incorporate ethical guidelines into artificial intelligence (AI) decision-making programs has been developed.
Understand the strengths and weaknesses of artificial intelligence (AI) and machine learning (ML) versus control theory, particularly model predictive control (MPC) for improving process and manufacturing applications and operations.
Engineers use artificial intelligence (AI) to magnify domain expertise and significantly cut time to end user.
More powerful and cost-effective computing combined with advancements in artificial intelligence (AI) are helping predictive maintenance to detect anomalies, which predicate a maintenance action when needed. Edge computing brings decision-making and intelligence as close to the process as possible.
While similar, artificial intelligence (AI), machine learning (ML) deep learning and neural networks have specific tasks and roles.
Achieving sustainability at the edge looks different for every organization. There are key steps to help narrow down which path to take to reach those sustainability goals.
Artificial intelligence (AI) can be used to enhance worker productivity by gathering information about their work performance and turning it into actionable data.
Artificial intelligence (AI) and automation have raised concerns about humans being replaced by machines in manufacturing, but the truth is they will add better and more meaningful jobs for humans.
Federated learning is a collaborative method for training a machine-learning model that keeps sensitive user data private.
Software-based artificial intelligence (AI) can be used to give robots abilities that allow them to straddle the flexibility gap between small batch manufacturing and high-volume automation.