the University of Lugano won a pattern recognition contest by outperforming both competing software systems and a human expert in identifying images in a database of German traffic signs. The winning programme accurately identified 99.46% of the images in a set of 50,000; the top score in a group of 32 human participants was 99.22%, and the average for the humans was 98.84%.
Deep learning was given a particularly audacious display at a conference last month in Tianjin, China, when Richard F Rashid, Microsoft’s top scientist, gave a lecture in a cavernous auditorium while a computer programme recognised his words and simultaneously displayed them in English on a large screen above his head. Then, in a demonstration that led to stunned applause, he paused after each sentence and the words were translated into Mandarin Chinese characters, accompanied by a simulation of his own voice in that language, which Rashid has never spoken. The feat was made possible, in part, by deep-learning techniques that have spurred improvements in the accuracy of speech recognition. Rashid, who oversees Microsoft’s worldwide research organisation, acknowledged that while his company’s new speech recognition software made 30% fewer errors than previous models, it was “still far from perfect.”
“Rather than having one word in four or five incorrect, now the error rate is one word in seven or eight,” he wrote on Microsoft’s web site. Still, he added that this was “the most dramatic change in accuracy” since 1979, “and as we add more data to the training we believe that we will get even better results.” One of the most striking aspects of the research led by Dr Hinton is that it has taken place largely without the patent restrictions and bitter infighting over intellectual property that characterise high-technology fields. “We decided early on not to make money out of this, but just to sort of spread it to infect everybody,” he said. “These companies are terribly pleased with this.”