Redefining AI Classification.
Random Contrast Learning (RCL™) is a universal classification algorithm that outperforms competitors with far lower costs in data, time, energy and hardware.
PrismRCL – Classification Optimized for Windows
PrismRCL allows you to train, iterate upon, and run inference against models from your CPU-based devices in a fraction of the time required by neural networks. Without sacrificing accuracy.
Build with the RCL API.
Implement Random Contrast Learning in your existing ML workflows employed for classification to reduce costs and increase accuracy.
RCL x GPT-4o ChatBot
By leveraging OpenAI’s GPT-4o API, we’ve created a prototype chatbot to allow an accessible window into the capabilities of RCL, inviting you to explore its potential in an intuitive, conversational format.
Latest Updates
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Lumina AI Strengthens Board of Directors with Addition of AI and Computing Visionary Trish Damkroger
Lumina AI, a leader in pioneering artificial intelligence and machine learning technologies, proudly announces the appointment of Trish Damkroger to its Board of Directors. Trish, a renowned expert in high-performance computing and AI, will bring her extensive...
Reducing AI’s Impact on the Environment
My 35-year career is a tale of two worlds in the same city. The first half of my career was spent in the environmental policy arena, in both the public and private sectors, while the second half has been in the technology industry. I started my career at the White...
Work with Lumina AI
RCL promises to advance AI and to expand its related markets as we improve existing AI workflows by replacing neural networks employed for classification, thereby reducing capital expenditure and increasing accuracy.
As CPU resources remain accessible, we believe that Random Contrast Learning will allow for a new market for AI and machine learning – enterprises without access to the capital or resources to build an AI practice.
For enterprises interested in deploying Random Contrast Learning at scale, please contact us for additional information.