Man and Machine Known Knowns: Understanding Smart Technology Part 4

Published on INFORMS Analytics Magazine (Joseph Byrum)

Author’s Note: This blog series Understanding Smart Technology – And Ourselves examines our relationship with advancing technologies and the fundamental choices we face. As we stand at the threshold of an uncertain future shaped by artificial intelligence, the author challenges readers to consider whether we should embrace these transformative changes or resist them in defense of our humanity. Drawing from historical patterns of technological adoption and resistance, the series promises to deliver nuanced perspectives on our technological trajectory, beginning with a comprehensive overview of our current understanding of smart technology and its implications for society. Read Part 3 where the author discusses the “known knowns” of the human mind.

The interaction between man and machine is the next stop on our journey toward a better understanding of what we know about smart technology. Most see robots as distinctly separate and increasingly intelligent beings with whom we either compete or cooperate. In this scenario, one known factor is that humans are biological, organic, living creatures with emotions – traits that nonliving robots cannot have.

So, which is superior, the human being or the robot? The answer is, it depends. At this point, there is evidence that for highly complex tasks, humans are still able to come out on top [1]. When it comes to an activity with well-defined rules, such as chess, the best computer can beat the best human. Examples of computers defeating humans at increasingly complex games include the following:  

• University of Alberta’s Chinook program beat Marion Tinsley, the world’s best checker player, in 1994.
• IBM’s Deep Blue beat chess master Garry Kasparov in 1997.
• IBM’s Watson beat the all-time top “Jeopardy” players in 2011.
• Google/DeepMind’s AlphaGo beat the champion Go player in 2017.
• Carnegie Mellon University’s Libratus and Lengpudashi beat human poker champions in 2017. 

Thinking in competitive terms might not be the most productive, as a cooperative human-machine effort is likely to be the most effective of all [2]. It is human nature to cooperate – not only with fellow humans but also with other creatures. The trust developed from bonds between humans and domesticated animals may be an “appropriate analog for human-robot trust,” as noted in a research paper on the topic [3]. 

Multiplier Effect of Technological Change

Meanwhile, all of these changes are happening so rapidly that their growth can be described as exponential. As noted in the website for Singularity University, a technology can be considered exponential if its power or speed double each year, or its cost must drop by half [4].

New technologies change how the factors of production – usually defined as land, labor, capital and entrepreneurship – are combined to produce a particular level of output. An improvement in production technology typically lowers the quantity of at least one of the factors needed to produce a given output, although it may increase one of the others. In smart technology, as in other economic phenomena, there is “no such thing as a free lunch.” Positive change in one domain can bring dislocations and distress in others.

Today, these forces are operating at an astonishing speed, and it takes time for humans to adapt to changes of this magnitude. We need time to adapt. This involves the passing of the old generation set in the old ways to a new generation comfortable and proficient with the new ways.

It may well be that the biggest challenge posed by the Fourth Industrial Revolution is not the magnitude of the change, but the rate at which these changes are happening. In fact, economists who are normally sanguine about technological disruption are concerned whether the change is now happening too fast. The usual response is the old jobs that disappear will be replaced by yet-to-be-imagined better new jobs, but they wonder whether that’s still true in this particular instance. 

Bringing it All Together

We know that smart technology is based in computer science, in particular the algorithms of computer programming. We have flexible and useful notions of human intelligence, and we understand the chemical components of organic life – something no smart machine can ever possess. The scientific community can tell us about the nature of the human brain and the machine brain. We also know a fair amount about human behavior in relation to machines. In addition, we have a good understanding of technology economics and its multiplier effect, posing challenges to humanity’s adaptive skills.

Those are the “known knowns” about smart technology. In the next blog we will examine the unknown knowns.

References

1. 1. Stroud, Jim, 2017, “Human vs Machine: Who Sources Best? The Results of the 2017 Grandmaster Competition,” March 21, 2017; https://www.eremedia.com/sourcecon/human-vs-machine-who-sources-best-the-results-of-the-2017-grandmaster-competition/
2. 2. Darwish, Kourosh, 2018, “Flexible Human-Robot Cooperation Models for Assisted Shop-Floor Tasks,” Mechatronics, Vol. 51, pp. 97-114, May 2018; https://www.sciencedirect.com/science/article/abs/pii/S095741581830048. See also https://encrypted.google.com/patents/US20160229068?cl=de.
3. Billings Deborah R., et al, 2012, “Human-Animal Trust as an Analog for Human-Robot Trust: A Review of Current Evidence,” (U.S. Army, March 2012); https://www.arl.army.mil/arlreports/2012/ARL-TR-5949.pdf.
4. “An Exponential Primer,” https://su.org/concepts/.