Infusing Machines with Intelligence - Part 3

As seen in Part 1 and Part 2 of this series, it is hard not to feel excited about machine learning. First, it empowers machines to teach themselves the tasks that humans can perform but find difficult to "teach" a computer via conventional coding (e.g. Secondly, it enables computers to perform tasks that far exceed human abilities, like analysing terabytes of data at lightning speed to unearth hidden patterns and make sense of them. But it is also hard not to feel some unease about the prospect of self-improving computer systems with increasingly human-like and super-human aptitudes, whether it is the threat of mass unemployment, the erosion of privacy, or simply the inability to understand, validate and trust the technologies that will increasingly impact our lives. These problems that artificial intelligence (AI) is throwing back at us are complex and multifaceted, and to tackle them requires concerted endeavours by our technologists, entrepreneurs, lawmakers and thinkers from all fields and walks of life. It will be a test of humankind's collective wisdom to ensure that our social institutions keep up with our technological progress. The advent of autonomous vehicles (AVs) illustrates the wide-ranging economic, legal and ethical questions that new technologies raise. AVs are already roaming the streets and conveying passengers in parts of the world, and many more are expected to hit the roads over the next five years as tech companies like Google, Baidu and Lyft race against incumbent automakers to make reliable and affordable self-driving cars. This is likely to dramatically alter the economics of transportation, from ownership rate to utilisation rate. It is estimated that in the US and the UK our cars on average are being driven just 5% of the time and they spend the remaining 95% in a garage or a car park.[1] That ratio may well be reversed if the availability of door-to-door transport is no longer linked to the availability of human drivers.