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In high-stakes scenarios like earthquake prediction, there’s no margin for error. The ability to process massive amounts of sensor data, extract patterns, and deliver accurate forecasts, all within a short time frame, can save lives and help mitigate damage.
Yet, traditional AI methods often fall short when it comes to the speed, energy efficiency, and precision required for such complex tasks. This is where GLAI (GreenLightning AI), developed by Qsimov, comes in.
In a recent independent study conducted by the Barcelona Supercomputing Center (BSC), GLAI was evaluated on the MareNostrum 5 supercomputer to address the challenge of seismic prediction. The objective was to assess whether GLAI could effectively train AI models to forecast both the intensity and areas that will be affected using large-scale seismic data. The experiment, designed and executed entirely by BSC researchers, demonstrated that GLAI delivers superior accuracy, reduces training times, and lowers computational demands compared to traditional neural network approaches.
The goal of this use case was to train an AI model capable of predicting both the intensity of earthquakes and the geographical areas most likely to be affected, using data from multiple seismic stations.
This required a model that could learn efficiently and accurately from large volumes of sensor data, maintaining performance while reducing computational time and cost.
With a 6% reduction in training error and the fastest training time among all tested models, GLAI clearly demonstrated superior performance. The outcome confirmed what our technology promises: better accuracy, significantly faster training, and reduced computational footprint compared to traditional neural networks.
GLAI’s exclusive feature, direct training when using mean squared error (MSE) as the loss function, allows it to bypass the iterative methods like gradient descent commonly used in conventional neural networks. This means it can reach the global minimum of the loss function, avoiding the pitfalls of local minima and delivering greater reliability in critical use cases.
While the seismic prediction use case is impressive, GLAI’s benefits extend far beyond any one application. Here's why more and more sectors are turning to this breakthrough AI architecture:
Accuracy without compromise: GLAI ensures high performance across complex tasks — from environmental forecasting to healthcare diagnostics — while offering transparent, traceable outputs.
Energy efficiency at scale: With its fast retraining, GLAI dramatically reduces compute time and energy use making it a pillar of Green AI. In other independent tests, GLAI demonstrated:
→ 239x lower energy use
→ 264x reduction in carbon emissions
→ 315x faster retraining
Federated learning ready: GLAI can be applied in federated learning setups, where data privacy is key, without losing model accuracy or scalability.
Direct training advantage: For specific loss functions (like MSE), GLAI offers non-iterative training with guaranteed global optimization a feature few AI frameworks can claim.
From predicting the next earthquake to reducing your carbon footprint, GLAI proves that performance, efficiency, and sustainability can go hand in hand.
Want to see how GLAI could enhance your AI systems?
