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September 20, 2024
By
Ginés Sánchez
Imagine teaching a machine to recognize every face in a crowded stadium. Instead of going over every face repeatedly, what if you could just teach it to recognize the new ones each time someone enters, without losing the knowledge of faces it’s already learned?
This is the revolutionary concept behind GreenLightningAI (GLAI), an AI system that transforms how models are retrained, improving efficiency and accuracy without sacrificing prior knowledge.
The GreenLightningAI (GLAI) model developed by Qsimov's CTO José Duato, introduces a groundbreaking approach to AI model development. At its core, GLAI leverages linear systems to emulate the behavior of complex feed-forward neural networks. This method drastically reduces the computational load required to retrain AI models, enabling organizations to incrementally update models without the need to process vast datasets repeatedly.
While traditional AI models often struggle with issues like catastrophic forgetting - Its dramatic name lives up to the disaster it can be- where old knowledge is overwritten during retraining, GLAI keeps previously learned information intact, ensuring consistent performance as new data is introduced. This innovation allows AI developers to continuously improve their models, even in decentralized environments such as IoT edge networks, with remarkable speed and energy efficiency.
GLAI offers a range of benefits that align with Qsimov's mission of optimizing AI development and making it more sustainable and transparent.
Rapid model combination
GLAI enables nearly instantaneous combination of different models, as long as they share the same architecture and a common sample subset selector. This capability opens doors to various applications, including incremental retraining, federated (re)training, and the dynamic creation of new models from pre-existing ones.
Faster training and retraining
With GLAI, training speeds are 2 to 4 times faster, while retraining can be 100 to 1000 times quicker, depending on the dataset size. This leap in performance is achieved through efficient model combination, resulting in significant cost savings and enhanced energy efficiency.
Accuracy
GLAI delivers performance levels comparable to traditional neural networks, with a marginal accuracy variation of ±1%. In some instances, GLAI even surpasses conventional models by improving accuracy through its approach of isolating the impact of different trainable parameters on outputs.
Explainability
In contrast to conventional neural networks, GLAI offers model explainability by clearly illustrating how inputs influence outputs (model results). This level of transparency is vital for industries that demand traceability and oversight in AI decision-making such as financial services or healthcare.
Quantum-Ready
GLAI is fully prepared to harness quantum computing for model training, allowing it to capitalize on future quantum advancements. This readiness ensures faster convergence times and even greater performance enhancements as quantum technology evolves.
Qsimov’s value proposition extends beyond just an AI model; it redefines how AI systems are designed and optimized. GLAI proves to be exceptionally valuable in scenarios where:
The AI model needs frequent or real-time retraining. AI models often require continuous updates to maintain accuracy and relevance, especially in dynamic environments where data changes rapidly. Real-time retraining ensures models stay current and perform optimally in ever-evolving conditions.
Retraining is costly due to its high frequency or large data volume. Frequent retraining can become expensive, particularly when dealing with vast amounts of data. The process consumes significant computational resources, leading to increased operational costs and energy consumption.
Retraining must be performed directly on edge devices, keeping data secure without leaving the device. Federated (incremental) retraining. In scenarios where data cannot leave the device for privacy or security reasons, retraining must occur directly on edge devices. This approach minimizes the risk of data breaches and ensures sensitive information remains protected.
Model explainability is necessary to bring it into production. To move from development to production, AI models need explainability. Clear insights into how models make decisions are essential for building trust, ensuring compliance, and gaining stakeholder approval, especially in industries where transparency is critical.
GLAI reimagines the possibilities of AI by enabling incremental retraining without the limitations of catastrophic forgetting. Its remarkable optimization slashes retraining times, enhances energy efficiency, and delivers explainability, transforming AI systems into powerful, transparent, and sustainable tools for modern businesses.
As industries increasingly rely on AI to drive automation and efficiency, GLAI sets the stage for a future where AI retraining is faster, smarter, and greener.
Discover how GLAI and Qsimov are leading this AI revolution and take your AI capabilities to the next level.
