Artificial intelligence (AI) systems promise transformative advancements, yet their growth has been limited by energy inefficiencies and bottlenecks in data transfer. Researchers at Columbia Engineering have unveiled a groundbreaking solution: a 3D photonic-electronic platform that achieves unprecedented energy efficiency and bandwidth density, paving the way for next-generation AI hardware.

The study, “3D Photonics for Ultra-Low Energy, High Bandwidth-Density Chip Data Links,” led by Keren Bergman, Charles Batchelor Professor of Electrical Engineering, is published in Nature Photonics.

The research details a pioneering method that integrates photonics with advanced complementary-metal-oxide-semiconductor (CMOS) electronics to redefine energy-efficient, high-bandwidth data communication. This innovation addresses critical challenges in data movement, a persistent obstacle to realizing faster and more efficient AI technologies.

A new kind of memristor mimics how the brain learns by combining analog and digital behavior, offering a promising solution to the problem of AI “catastrophic forgetting.”

Unlike traditional deep neural networks that erase past knowledge when learning something new, this innovative component may retain previous learning, just like our own brains.

Understanding “Catastrophic Forgetting” in AI.

In today’s column, I debunk the common myth that if we attain artificial general intelligence (AGI) the resultant AI will be a solo colossus or said-to-be “one big brain”

Let’s talk about it.

This analysis of an innovative AI breakthrough is part of my ongoing Forbes column coverage on the latest in AI, including identifying and explaining various impactful AI complexities (see the link here).