When popular science fiction depicts the ‘rise of machine intelligence’, it usually comes with lasers, explosions or, in some of the gentler examples, a mild philosophical dread. But there can be no doubt that interest in the possibilities of artificial intelligence (AI) and machine learning (ML) in real-life applications is on the rise, and new applications are popping up daily.
Millions of users globally are already engaging with AI using ChatGPT, Bard, and other AI interfaces. However, most of these users don’t realize that their cozy desktop exchanges with a curious AI assistant are actually driven by massive data centres all over the world.
Enterprises are investing in AI clusters within their data centres, building, training, and refining their AI models to suit their business strategies. These AI cores are composed of racks upon racks of GPUs (graphical processing units) that provide the incredible parallel processing power that AI models require for the exhaustive training of their algorithms.
With the data sets imported, inference AI analyzes that data and makes sense of it. This process determines whether an image contains a cat or a small dog, based on its training of what characteristics are common to cats but not dogs. Then, generative AI can process that data to create entirely new images or text.
It’s this ‘intelligent’ processing that has captured the imaginations of people, governments, and enterprises everywhere. However, creating a useful AI algorithm requires vast amounts of data for training purposes, and this is an expensive and power-intensive process.
