AI
Few buzzwords carry as much weight – or cause as much confusion – as artificial intelligence. We talk about algorithms, machine learning models and autonomous agents as if they were interchangeable. Yet at the heart of modern AI lies a distinct, transformative subset of technology: deep learning.
To understand where corporate tech stacks are heading, we first need to unpack how neural networks evolved from theoretical academic experiments into the engine powering everything from consumer smartphones to multibillion-dollar enterprise platforms.
Deep learning demystified To get a clear grasp on the hierarchy, it helps to look at how these technologies layer on top of one another. At the broadest level, AI represents any technique that enables computers to mimic human behaviour. Step inward and you reach machine learning, which is the capacity for systems to learn and improve from data without being explicitly programmed.
Step inward once more and you arrive at deep learning. This specialised branch extracts complex patterns from vast quantities of data using multi-layered artificial neural networks.
While neural networks are far from a new invention – having existed conceptually since the 1950s – we are currently experiencing a resurgence. This boom, according to the Massachusetts Institute of Technology
( MIT) is fuelled by three converging forces: big data( unprecedented volumes of information and easier storage), hardware acceleration( specialised GPUs provided by hardware giants like NVIDIA and AMD) and software innovation( refined training techniques and novel model architectures).
As Alexander Amini, researcher at MIT, explained when reflecting on the evolution of the field in an MIT introductory talk:“ Deep learning is so exciting because it enables us to learn those rules that are traditionally
148 September 2026