Deep Learning

In technical analysis, few concepts are as foundational as support levels — those invisible lines where prices tend to stop falling, bounce back, and spark new rallies. For decades, traders have relied on hand-drawn trendlines, Fibonacci ratios, and moving averages to guess where those turning points might be. But what if the real market structure is too complex, too dynamic, and too subtle for static rules? Enter DeepSupp, a new deep learning architecture that doesn’t guess support zones — it discovers them. By analyzing evolving market correlations through attention mechanisms and clustering latent embeddings, DeepSupp offers a glimpse into a future where support level detection is less of an art, and more of a science. ...

In the age of Transformers and neural nets that write poetry, it’s tempting to assume deep learning dominates every corner of AI. But in quantitative investing, the roots tell a different story. A recent paper—QuantBench: Benchmarking AI Methods for Quantitative Investment1—delivers a grounded reminder: tree-based models still outperform deep learning (DL) methods across key financial prediction tasks. ...

Crunch Time for AI: Photonic Chips Enter the Menu In the diet of modern artificial intelligence, chips are the staple. For decades, CPUs, GPUs, and more recently TPUs, have powered the explosion of deep learning. But what if the future of AI isn’t just about faster silicon—it’s about harnessing the speed of light itself? Two recent Nature papers—Hua et al. (2025)1 and Ahmed et al. (2025)2—offer a potent answer: photonic computing is no longer experimental garnish—it’s becoming the main course. ...