It’s all keen and mean on the artificial intelligence (AI) front in China, which is now vying with the United States as the top dog in the field. US companies can still boast the big cheese operators, but China is making strides in other areas. The UN World Intellectual Property Organisation’s Thursday report found that IBM had, with 8,920 patents in the field, the largest AI portfolio, followed by Microsoft with 5,930. China, however, was found dominant in 17 of 20 academic institutions involved in the business of patenting AI.
The scramble has been a bitter one. The Trump administration has been inflicting various punitive measures through tariffs, accusing Beijing of being the lead thief in global intellectual property matters. But it is also clear that China has done much to play the game.
“They are serious players in the field of intellectual property,” suggests WIPO Director-General Francis Gurry.
Machine learning is high up in this regard, as is deep learning, which saw a rise from a modest 118 patent applications in 2013 to a sprightly 2,399 in 2016. All this is to the good on some level, but the ongoing issue that preoccupies those in the field is how best to tease out tendencies towards bias (racism, sexism and so forth) that find their way into machine-learning algorithms. Then comes that problem of technology in the broader service of ill, a point that never really goes away.
In other areas, China is making springing efforts. Moving in the direction of developing an AI chip has not been missed, propelled by moves away from crypto mining.
“It’s an incredibly difficult to do,” claims MIT Technology Review senior editor Will Knight. “But the fact that you’ve got this big technological shift like it once in a sort of generation one means that it’s now possible, that the playing field is levelled a little bit.”
The nature of technological advancement often entails a moral and ethical lag. Functionality comes before philosophy. AI has been seen to be a fabulous toy-like thing, enticing and irresistible. But what is good in one field is bound to be inimical in another. The implications for this should be clear with the very idea of deep learning,