There is little question that technological progress is happening fast in electronics and that technological progress is compounding exponentially in this area. The notion that the
35
amount of transistors you can purchase for a fixed amount of money roughly doubles every 18 months, is popularly known as Moore's law (as it was first implied by a fitted line made on a graph by Intel co-founder Gordon Moore in 19653). But technological progress has been taking place across the board in electronic goods, as can be seen in the graph below.
Figure 9 – The exponential growth of different information technologies (Brynjolfsson & Mcafee, The Second Machine Age, 2014)
Exponential growth is of course a very powerful thing, with many of these lines
increasing 1000 fold over a decade or more, and every improvement of course potentially unlocks possible applications for new inventions.
Additionally, as costs of electronic equipment have plummeted, investment in IT infrastructure has been on the rise in companies.
3 The economist, http://www.economist.com/news/technology-quarterly/21651928-lithium-ion-battery-steadily-improving-new-research-aims-turbocharge?fsrc=scn/tw/te/pe/ed/chargeofthelithiumbrigade
36
Figure 10 - IT investment in millions nominally and per employee.
(McAfee, Andrew McAfee's Blog, 2011)
The graphs speak for themselves and likely confirm what the reader might already be aware of; computing technology is improving and changing the job market fast. But what the reader might not be aware of is how these trends are starting to extend their influence into the field of robotics and how they have powerfully extended the applications of robotics.
The industrial robots we are used to today, the ones that replaced the majority of workers of the car factories, work blindly, mindlessly repeating the same lifting, cutting or welding maneuver to the same standard sized pieces of metal over and over, completely oblivious to the world around them. But with advent of consumer electronics mass produced on a scale never before seen, the capabilities of electronics and sensor equipment has sky rocketed, all the while prices have been plummeting.
As an example of this I have compiled data on state of the art phone cameras in terms of mega pixel per dollar over 15 years (Figure 11). As this figure shows, consumers? now get an additional two or three orders of magnitude for their money than what they did a decade and a half ago. Furthermore, this is not accounting for all the other improvements that a modern smart phone has compared to the earlier feature phones. The 2013 phone (Nokia Lumina 1020) not only has a camera two orders of magnitude better than its 1999
37
counterpart (Kyocera VP-210), it also has an accelerometer, a gyro, proximity sensors, a compass, a barometer, a secondary camera, GPS, FM radio, WiFi, Bluetooth, touch screen, modern software among many other features. The figure below reflects price per mega pixel, but a similar graph could just as well have shown CPU power, screen
resolution, memory capacity, storage capacity, and so on. So even though figure 11 does not by any means give justice to all the ways a modern smart phone is much improved compared to the phones of 15 years ago, we still observe a very strong trend.
Figure 11 - Price per Mega Pixel in mobile phones over time. Data collected by the author. Phone names are displayed for the reader to scrutinize.
All these improvements in sensors and all their reductions in prices mean that robots are
“waking up”, sensing and making decisions based on the environment around them. Take for example the self-driving car, an area where Google is arguably at the forefront. By 2014 Google’s fleet of self-driving cars had driven more than 1,1 million self-driven kilometers, with only one accident, being rear ended by a human driver while the car was stopped at a red light (Google, 2014);(Markoff, 2010). Although the technology is not ready for mass production yet (the sensor suite reportedly costs 150.000 dollars), this
1 10 100 1000 10000
Mega Pixel / 2013 USD
38
proves that self-driving cars have the potential to be much safer than human controlled ones. The first fully self-driving mass produced car we will see, will perhaps be from the Silicon Valley car company Tesla Motors. The company’s CEO Elon Musk aspires to be the first on the market with a self-driving car and he has promised we will see them within five years (Wade, 2014). And private cars are of course not the only area of transportation that is poised to be revolutionized. Trains, planes, ships, busses, trucks, taxis and so on are also likely to be automated in the not so distant future. This is very important because, in the US the “transportation and material moving industry” employed 9 million people or as much as 6% of the total US workforce in 2014 (Us Bureau of Labor Statistics, 2015). And it is not only transportation that can be automated.
The food industry also looks like a candidate for major disruption. Within the last year McDonald’s and other major US restaurant chains have announced they will be deploying iPad like tablet systems for customer self-ordering (Thibodeau, 2014). At the same time, the first fully automated burger machine, capable of churning out a freshly made
hamburger in 10 seconds has been made. The machine allegedly produces cheaper, more sanitary and cheaper burgers, with vegetables freshly cut on demand (Love, 2014). It does not take much imagination then, to imagine a completely automated food vending
machine, with dishes made right on the spot. Perhaps one could make the
counterargument that at least the part of the food industry that is considered more luxurious than McDonald’s will not change to this “automated chef” or will adapt this technology much slower. But the adaptation of for example a tablet for ordering being located at customers table as a supplement to old fashioned “waiter ordering” would not seem an unimaginable scenario at even the most expensive of restaurants. In this way, technology does not necessarily have to automate an industry completely in order to have a noticeable effect, especially since “food preparation and serving related occupations”
occupies a similar amount of workers as the aforementioned transportation industry.
39