Modern Artificial Intelligence is undeniably powerful. It can generate stunning images, write complex code, and find patterns in data at a scale beyond human comprehension. But in the race for this remarkable performance, the AI industry has embraced a dangerous trade-off: we have exchanged understandable, verifiable rules for opaque, Read more
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From Measurement to Mechanism: The Landmark Realization in Fractional Scaling Digital Signal Processing
A landmark realization in science often occurs when a passive, descriptive measurement of a system is discovered to be the key to its active, underlying mechanism. This article details such a realization at the heart of Fractional Scaling Digital Signal Processing (FSDSP). We explore the journey of the Read more
Beyond Convolution: How FSDSP’s Patented Method Unlocks Fractional Calculus for AI
As engineers in AI and DSP, we treat convolution as a fundamental constant. It’s the bedrock of filtering and the workhorse of deep learning. But for systems requiring high precision and the modeling of real-world physics, our reliance on direct, time-domain convolution is a significant bottleneck. This reliance forces Read more
Beyond the Black Box: Why the Future of AI Relies on 300-Year-Old Math
Modern Artificial Intelligence is miraculous. It can write poetry, generate stunning images, and identify patterns in datasets larger than we can comprehend. Yet for all its power, today’s dominant AI has a fundamental problem that puts the entire future of AI at risk: it’s a black box.
We Read more
AI’s Crisis of Trust: A New Paradigm for Verifiable Intelligence
Modern Artificial Intelligence is undeniably powerful. It can generate stunning images, write complex code, and find patterns in data at a scale beyond human comprehension. But in the race for this remarkable performance, the AI industry has embraced a dangerous trade-off: we have exchanged understandable, verifiable rules for opaque, Read more
From Measurement to Mechanism: The Landmark Realization in Fractional Scaling Digital Signal Processing
A landmark realization in science often occurs when a passive, descriptive measurement of a system is discovered to be the key to its active, underlying mechanism. This article details such a realization at the heart of Fractional Scaling Digital Signal Processing (FSDSP). We explore the journey of the Read more
Beyond Convolution: How FSDSP’s Patented Method Unlocks Fractional Calculus for AI
As engineers in AI and DSP, we treat convolution as a fundamental constant. It’s the bedrock of filtering and the workhorse of deep learning. But for systems requiring high precision and the modeling of real-world physics, our reliance on direct, time-domain convolution is a significant bottleneck. This reliance forces Read more
Beyond the Black Box: Why the Future of AI Relies on 300-Year-Old Math
Modern Artificial Intelligence is miraculous. It can write poetry, generate stunning images, and identify patterns in datasets larger than we can comprehend. Yet for all its power, today’s dominant AI has a fundamental problem that puts the entire future of AI at risk: it’s a black box.
We Read more