Posts in February
Artificial Intelligence (AI) – the capability of a machine to imitate intelligent human behaviour – has made great inroads into the automotive, aviation, and other highly technical manufacturing industries in the last few decades. However, those that rely on human dexterity, such as clothing manufacture have remained relatively unchanged; mostly because their response to price pressure has been squeezing labour costs. Investment in new machines and processes has taken second place to offshoring; moving manufacture to lower priced economies where human labour is cheap.
But now even that may be about to change with the advent of a sewing robot that it is claimed can assemble an entire garment from scratch. If it lives up to its inventor’s claims it could bolster the hope that domestic factories in the US and UK might be able to compete once again. But it won’t bring back jobs!
So, automation, having eliminated many manufacturing and assembly jobs over the last couple of decades, will soon remove another tier of human employment. But this time they won’t be so called ‘blue collar’ production jobs. Commentators and futurologists predict that artificial intelligence (AI) is set to take over the service sector – then the professions!
As Dhaval Joshi, economist at BCA Research, has noted, it is not going to be the low-paid jobs in the service sector such as cleaning, gardening, carers, bar staff or cooks, whose jobs are most at risk. That’s because machines find it hard to replicate the movements of humans in everyday tasks.
“The hard problems that are easy for AI are those that require the application of complex algorithms and pattern recognition to large quantities of data – such as beating a grandmaster at chess”, says Joshi. “Or a job such as calculating a credit score or insurance premium, translating a report from English to Mandarin Chinese, or managing a stock portfolio.”
Seen in this light, the looming threat is obvious. The first army of machines wiped out well-paid jobs in manufacturing; the second army is about to wipe out well-paid jobs in the service sector. In many cases, the people who will be surplus to requirements will have spent many years in school and university building up their skill (1).
Could it go further? We know that machines have beaten humans in chess, draughts, and most recently in the ancient game of Go. But, more significantly, a machine has just beaten four professional poker players at their own game. The importance of this development lies in the fact that poker is an imperfect information game — similar to the real world where not all problems are laid out. The difficulty in figuring out human behaviour is one of the main reasons why poker was considered immune to machines.
The machine, developed by Carnegie Mellon University (CMU), and called Libratus, employs a problem solving algorithm that can be used in any situation where information is incomplete, including business negotiation, military strategy, cyber security and medical treatment. But, what next, if a machine can learn the ability to reason and bluff?
Over the last half-dozen years, deep learning, a branch of artificial intelligence inspired by the structure of the human brain, has made enormous strides in giving machines the ability to intuit the physical world. Three years ago, Microsoft’s chief research officer impressed attendees at a lecture in China with a demonstration of deep learning speech software that translated his spoken English into Chinese, then instantly delivered the translation using a simulation of his voice speaking Mandarin—with an error rate of just 7%.
So, how will these developments affect the membership sector? Can trade associations acclimatise to the new reality, and – assuming we still have them – how can we help members adapt?
©2017 M J Hoare