The picture above is ASCI Red, the world’s fastest supercomputer in 1999-2000. It was about the size of a large tennis court, sucked a couple of MW and cost around $55M (it went through various incarnations). And that’s not to mention the staff of acolytes and air-conditioned buildings required to make it work. Its delivered performance was about 2.4 TFlops (Thousand Billion Floating Point Operations per second), with a theoretical maximum of around 3.2TFlops, delivered by an array of nearly 10,000 processors, all chuntering away in parallel.
AI (that’s Artificial Intelligence – I have to be clear here as I live in a farming community and conversations have been known to take a strange turn) is a flavour of the moment and is riding high on the arm-waving curve of the hype cycle. We’ve been here before though – as a notion, AI has been through more loops of the hype cycle than most technologies, with successive waves of mutually reinforcing innovation and fiction conspiring to promise more than contemporary understanding could deliver.
Rewind: Sixteen years or so ago, I was interested in how we use software to help us solve the compound, iterative and ever-changing problems we face every day: juggling complex trip schedules, working out where we need to be and when to co-ordinate with our friends or colleagues and, of course, how we find out about stuff that we’d want if only we knew to ask for it. I’m still thinking about it.
Here’s a little history: in the early nineties, much of my consultancy work orbited (often eccentrically) around a binary model: the development of new technologies and helping clients to understand how those technologies could help their businesses and to work out how and when to jump in. It still does. Continue reading Hype, Reality and Expectation