Over the last 20 years, inflation-adjusted U.S. manufacturing output has increased by almost 40 percent, and annual value added by U.S. factories has reached a record $2.4 trillion. While there are fewer jobs, more is getting done.
Today’s industrial bots are typically programmed to do a single job very precisely and accurately. But each time a production run changes, the robots then need to be reprogrammed from scratch, which takes time and technical expertise.
Machine learning offers a way to have a robot reprogram itself by learning how to do something through practice. The technique involved, called reinforcement learning, uses a large or deep neural network that controls a robotic arm’s movement and varies its behavior, reinforcing actions that lead it closer to an end goal, like picking up a particular object. And the process can also be sped up by having lots of robots work in concert and then sharing what they have learned
Fanuc, one of the world’s largest makers of industrial robots, announced that it will work with Nvidia, a Silicon Valley chipmaker that specializes in artificial intelligence, to add learning capabilities to its products.
In a 2014 trip to factories in Shenzhen, 60 percent of it was automated and 40 percent of it was still people. And it’s all a question of choice. You say, “is that just because of low cost?” No, no. These are actually high-pay, high-skill jobs. Adaptability is key, and people are more adaptable. So when they set up the machine line and it’s all machines, there is a huge amount of retooling to shift from line one to line two, whereas the people are much more easy to shift.
Therefore, adding AI to make the robots more adaptable will mean fewer people needing for retooling to shift lines.
There needs to be faster training and better education for people. Augmented reality can be used to help people become productive more quickly.
SOURCES -Technology Review, McKinsey, TechCrunch