MLiglon’s Integer Gate Logic (IGL) — The Post-Backpropagation Era Begins Here

US Patent 12,242,946-B1

The Crisis in AI: A $2 Trillion Industry Held Hostage by 1970’s Math!

Artificial intelligence is hitting a wall. Despite breakthroughs like generative AI and large language models (LLMs), the industry remains shackled to backpropagation, a 50-year-old algorithm that devours $100B+ annually in compute costs, guzzles energy (surpassing entire countries), and collapses under the weight of noisy data and unsustainable model sizes. Even NVIDIA’s GPUs—workhorses of modern AI—struggle to keep pace with demand. The result? AI’s growth is unsustainable without a radical reinvention of its core engine.

MLiglon’s Revolution: Integer Gate Logic (IGL) — The First True Alternative to Backpropagation.

MLiglon has cracked the code with patented Integer Gate Logic (IGL), a fundamentally new approach to machine learning that outperforms backpropagation on every axis that matters:

Accuracy: Achieves higher precision on benchmark datasets, even with sparse or corrupted inputs (e.g., medical imaging, autonomous vehicles).
Efficiency: Reduces memory usage by using integers instead of floating point values, slashes energy consumption, and accelerates training with smaller models—critical for enterprises battling escalating cloud bills.
Scalability: Possibility to train models larger than current hardware limits using existing GPUs, unlocking AI capabilities previously deemed impossible.
Explainability: Eliminates “black box” opacity with Boolean logic-driven transparency, meeting urgent regulatory demands in healthcare, finance, and defense.
Ideal for TinyML, EdgeAI, and IoT devices: Smaller and more accurate models will fit into and run more efficiently on memory and computationally limited devices.

MLiglon Corporation

MLiglon AI (creator of Integer Gate Logic, “IGL”) is a Texas based corporation offering the next generation of products and platforms for training neural networks. Using patented, proprietary technology, IGL networks train faster, predict more accurately, reduce energy consumption, provide enhanced parallelization and scalability, and promote model transparency. IGL addresses all of the most pressing AI industry needs, including faster and better performance models at lower cost. IGL is the first truly revolutionary AI neural network architecture in 50 years, and could make backpropagation and other gradient-descent training solutions a thing of the past.