Robots, work and the U.S. Presidential Election - Part 1

"Will robots take our jobs?" is one of the first questions I'm asked when I talk about my Centre's work in robotic vision (the application of computer vision to robotics). More recently the question has slightly changed to, "When will robots take our jobs?"

As someone who works in a lab full of robots, and sees the many limitations of robots every day, for example, our drinks-carrying robot - trained to avoid obstacles (including people who want a drink), it is easy to dismiss these concerns. I used to joke that if the job involved opening doors or going up or down stairs, humans were in no danger any time soon. One of my favourite videos is a compilation of robots failing to complete simple tasks while competing in the 2015 DARPA Robotics Challenge.


But while robots are a long way from having the balance and dexterity of humans, people do have cause for concern. The pace of technological change is overwhelming. Only 10 years ago the iPhone did not exist and the first autonomous vehicles bristled with so many sensors, and so much onboard computing hardware, they would have struggled to carry a passenger.

CMU's Tartan Racing Team won the DARPA Urban Challenge in 2007 with an extensively modified Chevrolet Tahoe. The first autonomous vehicle to navigate a 96 km course in less than 6 hours

Today your smart phone is orders of magnitude more powerful than the mainframe computers that put man on the moon, and autonomous vehicles are sleek and ready for passengers.

Google's Waymo in 2016 - no longer bristling with sensors and with more than 3 million km self-driven

While it takes human's 3-4 years to gain enough mastery of a subject to earn a university degree, IBM Watson can process 500Gb of data, the equivalent of reading a million books, per second. And while we humans can gain competence through our years of work experience, deep learning enables intelligent machines to also learn from their experiences, or indeed from ours (see robots learning to cook by watching YouTube videos). The only difference is that once one machine learns, that knowledge can be transferred to all networked machines, in much the same way that the IoT allows electric cars like Tesla to receive updates over the internet.

Imagine being able to share all the information you have gained from your life experiences with every other human on the planet. We can hope we are nearing Ray Kurzweil's Singularity, where we humans will transcend our biological limitations. In the meantime the transition to a time when there is no clear distinction between human and machine is likely to be tumultuous as robots do take some jobs and people struggle to redefine work and their place in the world.

For more on robots, work and what this has to do with the US presidential election see Part 2, coming soon.