Rick Delgado had an interesting article on how the widespread availability of machine learning will facilitate the rollout of the Internet of Things (IoT). Intuitively it makes sense; as algorithms become widely understood and field tested, they evolve from black magic to tools in the engineering kit. You can see this phenomenon in automotive safety technology. In the mid 1990s I was working on machine vision algorithms for automotive applications. Everything was new and exciting; there were a few standard theories, but they had barely been tested at any scale and the processing hardware hadn’t caught up to the data demands. Now as the Wall Street Journal reports, Toyota is making collision-avoidance gadgets standard on almost every new model. One driver is the reduced price of the cameras and radars, but I think a bigger driver is the trustworthiness of the autonomous vehicle algorithms that can reliably sense a possible collision.
Of course, here at WANdisco the IoT is of much interest. For all of this new streaming data to be useful, it has to be ingested, processed, and used, often at very high speeds. That’s a challenge for traditional Hadoop architectures – but one that we’re quite prepared to meet.