“The history of work - particularly since the Industrial Revolution - is the history of people outsourcing their labor to machines,” notes a recent article in the Harvard Business Review, - AI Should Augment Human Intelligence, Not Replace It, - by National University of Singapore professor David De Cremer and chess grandmaster Garry Kasparov. “While that began with rote, repetitive physical tasks like weaving, machines have evolved to the point where they can now do what we might think of as complex cognitive work, such as math equations, recognizing language and speech, and writing. Machines thus seem ready to replicate the work of our minds, and not just our bodies.”
Throughout the Industrial Revolution there were periodic panics about the impact of automation on jobs, going back to the so-called Luddites, - textile workers who in the 1810s smashed the new machines that were threatening their jobs. Automation does indeed substitute for labor. However, automation also complements labor, raising economic outputs in ways that often lead to more long-run employment, not less.
Most jobs involve a number of tasks or processes. Some of these tasks are more routine in nature, while others require judgement, social skills and other human capabilities. The more routine, rules-based the task, the more amenable it is to automation. But just because some of the tasks have been automated, does not imply that the whole job has disappeared. To the contrary, automating the more routine parts of a job will often increase the productivity and quality of workers by complementing their skills with machines and computers, as well as enabling them to focus on those aspect of the job that most need their attention.
AI is already superior to humans in a number of tasks, but the future of work isn’t a zero-sum game in which there can only be one winner. “The question of whether AI will replace human workers assumes that AI and humans have the same qualities and abilities - but, in reality, they don’t,” noted De Cremer and Kasparov. “AI-based machines are fast, more accurate, and consistently rational, but they aren’t intuitive, emotional, or culturally sensitive. And, it’s exactly these abilities that humans posses and which make us effective.”
“In general, people recognize today’s advanced computers as intelligent because they have the potential to learn and make decisions based on the information they take in. But while we may recognize that ability, it’s a decidedly different type of intelligence what we posses.” According to the article, there are three different kinds of AI: artificial (AI1), authentic (AI2), and augmented intelligence (AI3).
Artificial intelligence (AI1). “In its simplest form, AI is a computer acting and deciding in ways that seem intelligent.” Also referred to as soft, narrow or specialized, AI is inspired by, but doesn’t aim to mimic the human brain. AI1 is generally based on machine learning methods, that is, on the analysis of vast amounts of information using powerful computers and sophisticated algorithms, whose results exhibit qualities we tend to associate with human intelligence.
Technology advances have enabled selected AI1 applications to achieve or surpass human levels of performance in specific tasks, including image and speech recognition, skin cancer classification, breast cancer detection, and prostate cancer grading. “In addition, contrary to humans, AI never gets physically tired and as long as it’s fed data it will keep going. These qualities mean that AI is perfectly suited to put to work on lower-level routine tasks that are repetitive and take place within a closed management system. In such a system, the rules of the game are clear and not influenced by external forces.”
Authentic intelligence (AI2). In 1994 the Wall Street Journal published a definition of intelligence which reflected the consensus of 52 leading academic researchers in fields associated with human intelligence: “Intelligence is a very general mental capability that, among other things, involves the ability to reason, plan, solve problems, think abstractly, comprehend complex ideas, learn quickly and learn from experience. It is not merely book learning, a narrow academic skill, or test-taking smarts. Rather, it reflects a broader and deeper capability for comprehending our surroundings - ‘catching on,’ ‘making sense’ of things, or ‘figuring out’ what to do.” This is a very good definition of general intelligence, - the kind of intelligence that’s long been measured in IQ tests, and that, for the foreseeable future, only humans have.
“Contrary to AI abilities that are only responsive to the data available, humans have the ability to imagine, anticipate, feel, and judge changing situations, which allows them to shift from short-term to long-term concerns. … In an open management system, the team or organization is interacting with the external environment and therefore has to deal with influences from outside. Such work setting requires the ability to anticipate and work with, for example, sudden changes and distorted information exchange, while at the same time being creative in distilling a vision and future strategy. In open systems, transformation efforts are continuously at work and effective management of that process requires authentic intelligence.”
Augmented Intelligence (AI3). “[W]e believe that it will be the combination of the talents included in both AI1 and AI2, working in tandem, that will make for the future of intelligent work. It will create the kind of intelligence that will allow for organizations to be more efficient and accurate, but at the same time also creative and pro-active. This other type of AI we call Augmented Intelligence.”
In the past two decades we’ve seen a number of examples where the combination of humans and machines make better decisions that either one on its own. Sabermetrics, - the use of statistics in baseball to project a player’s performance and career, - is one such prominent example. Sabermetrics was popularized by Michael Lewis in Moneyball, - his bestseller book later turned into a film. Sports analytics are now used by just about every professional team in the world.
When Moneyball first came out in 2003, many viewed it as a story about the conflict between the traditional approach of the scouts, - the professional talent evaluators who learn about the players first-hand by meeting them in person and watching them play, - versus the new approaches being introduced by the statheads, - who mostly rely on sophisticated statistical analysis to predict future performance.
But years later, - as Nate Silver explained in his 2012 bestseller The Signal and the Noise, - there was enough data to compare the performance of scouts versus more purely statistical approaches. The scouts’ predictions were about 15 percent better than those that relied on statistics alone. The good scouts, as it turns out, use a hybrid approach combining statistics with whatever else they learn about the players. Statistics alone cannot tell you everything you want to know about a player, and the additional personal evaluations of the scouts make a significant difference.
Chess is another prominent example, one where article co-author Garry Kasparov has some unique, personal insights. In 1997, then reigning world champion Kasparov lost a chess match to IBM’s Deep Blue supercomputer. Deep Blue’s victory over Kasparov was a major AI milestone. But having shown that machines could beat chess grand masters, the research experiment was over. Kasparov himself realized that the game of chess could now be approached not simply as an individual effort, but also as a collaborative effort between humans and machines. He then pioneered the concept of advanced chess, in which humans use computer tools to augment their chess playing capabilities as they compete against other such man-plus-machine teams.
Experience with advanced chess tournaments has shown that the strength of the human players, - whether grandmasters or amateurs, - is not what determines the winning team. Rather, it’s the quality of the partnership that matters, that is, the process of how players and computers interact. As Kasparov succinctly put it “Weak human + machine + better process was superior to a strong computer alone and, more remarkably, superior to a strong human + machine + inferior process.”
“The enhancing and collaborative potential that we envision stands in stark contrast to the zero-sum predictions of what AI will do to our society and organizations,” wrote the authors in conclusion. “Instead, we believe that greater productivity and the automation of cognitively routine work is a boon, not a threat. After all, new technology always has disruptive effects early on in the implementation and development phases and usually reveals its real value only after some time.”
Not sure I understand authentic intelligence, but it is an interesting concept.
Posted by: Jim Spohrer | July 18, 2021 at 07:52 PM