AutoML

Autonomous AI Research Agents: Unleashing Self-Improving Machine Learning on a Single GPU Imagine a world where machine learning research no longer requires endless hours of human debugging, hypothesis testing, and late-night experiment runs. Instead, AI agents take the wheel, autonomously iterating on code, running experiments, and stacking improvements overnight—all on a single consumer-grade GPU. This isn’t science fiction; it’s the reality introduced by Andrej Karpathy’s groundbreaking autoresearch project, which has sparked a revolution in how we think about AI-driven development.[1][2] ...

Demystifying Auto-Unrolled Proximal Gradient Descent: Revolutionizing Wireless Optimization with AI Smarts Imagine you’re trying to tune a massive radio tower array to beam internet signals precisely to your smartphone, even in a crowded stadium. Traditional math-heavy algorithms chug through hundreds of iterations—like a marathon runner pacing slowly to the finish line. But what if AI could sprint there in just a few smart steps, using far less data and explaining exactly how it did it? That’s the promise of Auto-Unrolled Proximal Gradient Descent (Auto-PGD), a breakthrough from the paper “Auto-Unrolled Proximal Gradient Descent: An AutoML Approach to Interpretable Waveform Optimization”.[6] ...