
Allan Martin
6.4K posts

Allan Martin
@tampabanker
Investor in #AI, #Banks, CEO and Co-Founder of Lumina AI, Chairman of Atlantic Merchant Capital.










AI’s energy and water demands are rising, but solutions exist. Lumina’s Random Contrast Learning (RCL) offers a more efficient alternative, reducing environmental impact without sacrificing performance. @WSJ on.wsj.com/3YJ15bB #SustainableAI

Excited to introduce PrismRCL™ 2.5.0! 🚀 Enhanced auto-optimization with new parameters New evaluation methods: softmax, chisquaredpair & more Faster image loading & improved caching Try it free for 30 days: lumina247.com/prismrcl-sign-… #LuminaAI #PrismRCL #AIforCPU #MLonWindows

🚀 Try our new prototype integrating RCL with GPT-4o! It showcases the versatility and effectiveness of our RCL algorithm in a user-friendly chat format. Choose an example model, upload your data, and review the results. Explore it here: lumina247.com/rcl-gpt-4o/ #RCL #GPT4o

Lumina AI adds AI expert Trish Damkroger, HPE's Chief Product Officer for HPC and AI, to its Board of Directors. prn.to/4dGc2Yz

Big News: Lumina AI is proud to announce that we are part of Microsoft for Startups Founders Hub 🎉 Discover how this partnership will accelerate the distribution of our flagship algorithm, #RCL, with insights from our CEO @TampaBanker and Board Member @edingle

@Lumina_AI_ is thrilled to join #IntelLiftoff! This journey with Intel paves the way for us to scale to new heights. Excited to deliver the capabilities of #LuminaRCL at scale and accelerate our growth and visibility within the #AIML community. 🚀 prn.to/3QTiUZS @intel



As AI's hunger for energy intensifies, we must confront the environmental toll. @edingle, author of our latest "Executive Insights" feature, spotlights a stark reality: the AI industry's burgeoning energy needs could soon rival the consumption of entire nations. 🧵

Another breakthrough for autonomous driving: Random Contrast Learning, a new CPU-based algorithm, performs much better than a neural network in autonomous simulators. The experiments: • A controlled simulation environment • An autonomous vehicle with an array of 8 sensors • A race track • Two side-by-side algorithms • 100 trials The algorithms were LuminaRCL (proprietary implementation of Random Contrast Learning) and an artificial neural network. The vehicle gradually learned to navigate the track using both algorithms. The Random Contrast Learning implementation completed a full lap with an 81% success rate after 15,000 training steps. The neural network required 150,000 steps to achieve a paltry 23% success rate. Here is more information about the experiments: bit.ly/RCLvsNN-CarSim… Thanks to the Lumina team for collaborating with me on this post and helping me understand PrismRCL, their desktop application you can use to run their Random Contrast Learning algorithm. You can try it out for free by going to this link: lumina247.com/prismrcl-sign-…
