Join our XPotential Community, future proof yourself with courses from our XPotential Academy, connect, watch a keynote, or browse my blog. The human brain operates on roughly 20 watts of power, or to put it another way, on a third of a 60 watt light bulb, in a space the size of, well, a human head. Meanwhile, the biggest machine learning algorithms use closer to a nuclear power plant's worth of electricity and racks of chips to learn. That's not to slander machine learning, but nature may have a tip or two to improve the situation. Luckily, there's a branch of computer chip design heeding that call right now and recently researchers in the UK spun up a million core computer that gets us closer to our goal of mimicking the human brain with all its energy efficiencies, in computer form. I am, of course, talking about revolutionary neuromorphic computers.

Created by scientists at Pompeu Fabra University in Spain the new system was trained using a gesture-recognising Myo armband that tracked the arm movements of a professional violinist as she used the Détaché, Martelé, Spiccato, Ricochet, Sautillé, Staccato and Bariolage bow techniques. Audio of the performances was recorded at the same time. The Machine Learning based algorithm then compared the arm movements to the corresponding audio, determining which movements created which sounds, within each technique. When the system was subsequently tasked with identifying the technique that a violinist was using, it could do so with an accuracy of over 94 percent. It is now hoped that once developed further the technology could be used to provide students with real-time feedback, showing them where their form deviates from that of the pros, and once the technology's refined then it won't be just constrained to teaching people how to play the violin – you can imagine it being used to help athletes up their game, and myriads of other applications. The research, which was led by David Dalmazzo and Rafael Ramírez, is described in a paper that was recently published in the journal Frontiers in Psychology.