Of all the statistical analysis techniques I receive news alerts for, the neural network flashes up on my screen most often. While I, like many of you, really enjoy the big-screen futuristic applications of neural nets–prediction of sun storms
is a splendid recent example–there is a quieter trend ramping up at a more down-to-earth level. The nano level,that is the itsy-bitsy, teeny-weeny, the molecular level.
For at least the past five years, the nanotechnology industry has been predicting and prototyping ways to incorporate neural networks into nano-machines. This innovation has proved to be very handy for sensing devices. The nano-sensor combines receptor particles with electronics controlled by a neural network algorithm. The neural net sorts through the sensor responses to uncover patterns that trigger alerts.
This year there was a flurry of media attention focused on one of these sensing technologies, the nano-nose
, which uses an array of nano-receptors coordinated by a neural network. These sensors are being promoted to sniff out everything from explosives to disease.
One indication of the expected adoption of applications that combine nano with neural is the advertising for neural network algorithms that can downsize to nano. But more than one of the nano-machine innovators has commented on the need to develop more robust statistical analysis techniques to improve the accuracy of the sensors. Which means that there will be more neural network to shrink, which means that the algorithms advertised today may already be outdated.
Whatever the commercial considerations and no matter how blasé we become about technological possibility, there is still a big wow factor in packing a high-powered computing technique into such infinitesimal space, and you can be certain the nano people will be harnessing neural networks to many new kinds of more-mini-than-micro machines.