Edge Computing, Embedded Systems
Machine learning facilitates turbulence tracking in fusion reactors
MIT researchers have developed a machine learning approach that can affect the energy generated during fusion reactions, with implications for reactor design.
Improving deep learning with light photonics
A new method uses optics to accelerate machine-learning computations on smart speakers and other low-power connected devices.
How AI and machine learning can drive sustainable 5G
Future networks have the potential to reduce the environmental impact of industries that consume high levels of energy, but the industry needs a holistic energy saving solution to maximize the benefits and create real impact.
AI is driving digital transformation in engineering
Artificial intelligence (AI) is driving digitalization when it comes to transforming the engineering sector.
Network pruning can skew deep learning models
North Carolina State University researchers learn that network pruning can adversely affect the performance of the model at identifying certain groups.
Machine, device learning on the edge
MIT researchers have developed a technique that enables AI models to continually learn from new data on intelligent edge devices like smartphones and sensors, reducing energy costs and privacy risks.
Formula tackles complex moral decision-making for AI
A blueprint for creating algorithms that more effectively incorporate ethical guidelines into artificial intelligence (AI) decision-making programs has been developed.
Is control theory better than AI for improving plant performance?
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.
Smarter energy measurements, faster, using AI
Engineers use artificial intelligence (AI) to magnify domain expertise and significantly cut time to end user.
Using edge machine learning for anomaly detection, predictive maintenance
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.
