The Internet of Thinking Things – Intelligence at the Edge

Via F1 journalist James Allen’s blog (Insight: Inside McLaren’s Secretive F1 Openerations Room, “Mission Control”), I learn that the wheel hub of McLaren’s latest MP4-31 Formula One car hacks its own data. According to McLaren boss, Ron Dennis:

Each wheel hub has its own processing power, we don’t even take data from the sensors that surround the wheel [that measure] brake temperatures, brake wear, tyre pressures, G-Forces – all of this gets processed actually in the wheel hub – it doesn’t even get transmitted to the central ECU, the Electronic Control Unit.

If driver locks a brake or the wheel throws itself out of balance, we’re monitoring the vibration that creates against a model that says, “if the driver continues with this level of vibration the suspension will fail”, or the opposite, “we can cope with this vibration”.

With artificial intelligence and machine learning modeling now available as a commodity service, at least for connected devices, it’ll be interesting to see what the future holds for intelligence at the edge – sensors that don’t just return data (“something moved” from a security sensor, but that return information (“I just saw a male, 6′, blue trousers, green top, leaving room 27 and going to the water cooler; it looked like… etc etc..”)

Of course, if you’re happy with your sensors just applying a model, rather than building one, that appears to be the case for the MP4-31 wheel hub, it seems that you can already do that at the 8 bit level using Deep Learning, as described by Pete Warden in How to Quantize Neural Networks with TensorFlow.

By the by, if you want to have a quick play with a TensorFlow learner, check out the TensorFlow Neural Network Playground. Or how about training a visual recognition system with IBM’s Visual Recognition Demo?