Increase speed and accuracy in machine learning with RCL. Optimized for CPU.
Explore how Random Contrast Learning breaks free from traditional neural network limitations to deliver more powerful and resource-efficient AI classification.
Maximize Flexibility, Minimize Complexity: The RCL Advantage
See how Random Contrast Learning (RCL) delivers unparalleled flexibility in machine learning, offering powerful solutions that adapt seamlessly to your unique challenges—without the complexity of traditional neural networks.
Random Contrast Learning (RCL) is transforming AI & Machine Learning.
Introducing Random Contrast Learning (RCL)
Random Contrast Learning (RCL) is a groundbreaking form of artificial intelligence that revolutionizes the way machines learn and classify data. Developed by Lumina AI, RCL offers a new perspective on machine learning, diverging from traditional resource-intensive neural network approaches.
What sets RCL apart?
RCL is designed to mimic the structures of human consciousness, drawing inspiration from Husserlian phenomenology. This approach focuses on how the mind draws distinctions and classifies objects, unlike traditional neural network styles of machine learning, making it a universally applicable classification algorithm at the heart of AI and machine learning.
This unique approach allows RCL to achieve superior accuracy in classification tasks while maintaining exceptional computational efficiency. By leveraging the power of conscious-inspired learning, RCL pushes the boundaries of what’s possible in artificial intelligence, offering a revolutionary solution for a wide range of AI applications.
Random Contrast Learning (RCL™ ) is a new style of machine learning invented in 2021 by Dr. Morten Middelfart, Sam Martin, and Ben Martin, who drew upon:
- 8 years of development by Lumina;
- 40 years of pioneering work in the research and development of systems designed to detect weak signals; and
- Engagement with the philosophical tradition of phenomenology.
Why Choose RCL?
Pretrain Large Language Models
Random Contrast Learning (RCL) accelerates large language model (LLM) pretraining directly on CPUs—dramatically cutting hardware expenses, reducing training times, and simplifying technical complexity. By bypassing traditional neural network requirements, RCL enables rapid model iteration, seamless scalability, and cost-effective deployment.
RCL makes AI Profitable.
RCL revolutionizes AI profitability by eliminating the need for specialized hardware and drastically reducing model training time, technical staffing requirements, and infrastructure costs.
By optimizing for CPUs, enabling rapid model creation, iteration and/or combination, while removing the need for traditional neural network architecture, RCL empowers companies to deploy machine learning technologies more efficiently and cost-effectively than ever before.
Do more with less.
RCL achieves superior performance while using fewer resources in data, time, energy, and hardware compared to traditional AI approaches.
One algorithm, countless applications.
As a universally applicable classification algorithm, RCL can improve a wide variety of existing AI workflows across different industries and use cases.
CPU-powered performance.
RCL runs on widely available CPU hardware, significantly reducing costs associated with expensive GPU chips typically required for AI applications.
Simple data preparation and parameter Auto Optimization.
Unlike traditional styles of machine learning, after your data is organized into class folders, no further pre-processing or data preparation is required. Tabular data does not need to be normalized.
Our auto optimize feature applies parameters best suited for your datasets.
Combine Insights, Protect Privacy.
RCL models can be combined without retraining, allowing users to gain collective insights without exposing sensitive data.
AI with a smaller footprint.
By utilizing CPU instead of GPU and requiring less energy for training, RCL significantly reduces the carbon footprint associated with AI operations.
Keep your data close.
Train models on local devices without the need for cloud access or data sharing, ensuring maximum data privacy and security.
Utilize RCL on your CPU-based devices.
With RCL, you can unlock powerful machine learning capabilities across different platforms tailored to your needs:
- PrismRCL (Windows Application): Ideal for users who prefer a command-line interface for training models on any CPU-based machine. Easily run inference, combine models, and leverage the full power of RCL through an optimized Windows environment.
- Chatbot (Experimental): Experience RCL within an interactive chat interface, blending natural language processing and classification in real-time.