![]() ![]() ![]() Indeed, just because humans can solve difficult problems requiring fluid intelligence does not mean humans can describe, let alone replicate, the mechanisms underlying decision-making that are defined as ‘fluid intelligence.’Ĭonsider some of the tasks at which A.I. While increasing intelligence in humans is remarkably difficult, even the most sophisticated models, continuously fed with nearly-limitless datasets, fail to prove to be adequate replacements for humans in utilizing fluid intelligence. But we don’t see strong evidence of that happening ( Chiang). were an activity like solving a set of math puzzles, we ought to see successful examples of it at the low end, where the problems are easier to solve. For instance, if increasing someone’s I.Q. Moreover, a substantial roadblock for researchers in developing machine fluid intelligence is that humans themselves are unable to convert their fluid intelligence to a ‘higher’ form. Those who have attempted to use a voice device like Amazon’s Alexa or Google’s Home products know even the most basic commands or requests can be incredibly frustrating. The capabilities of these machines to both run incredibly complicated programs as well as compute exponentially more tasks and calculations than a human, however, has not resulted in comparable gains for a machine’s fluid intelligence. Today, even consumer chips, like Apple’s M1 Max, have well over 50 billion transistors. The development of computer processing power has followed Moore’s Law since 1970, as the number of transistors on integrated circuits has (more than) doubled every two years. In fact, even during a global pandemic, American businesses are struggling to find enough workers, lowering production, limiting hours of operation, and increasing prices ( CNN). Yet 20 years since Brain’s prediction there is little evidence of automation causing widespread unemployment. ![]() Similarly, the World Future Society forecasted in 2007 that employees will be less valuable and compensation would decrease as humans would now face competition from machines, as, “businesses will hire whatever type of mind can do the work-robotic or human” (SHRM). According to Brain’s projections, in his essay ‘Robot Nation’, humanoid robots will be widely available by 2030…Brain estimates more than 50% of Americans could be unemployed by 2055 – replaced by robots” (Pinto 2003). In 2003, Marshall Brain claimed, “over the next 40 years robots will replace most human jobs. In contrast, crystallized intelligence refers to knowledge gained from prior learning of facts, skills, and experiences ( Psychology Today). Fluid intelligence is the ability to think flexibly, reason, and process novel information in real time. The concept of fluid versus crystallized intelligence dates to 1963, when it was introduced by one of the most influential psychologists of the 20th century, Raymond Cattell (1905-1998). While computers are far more efficient than humans at crystalized tasks, such as information recall, pattern recognition, and statistical inference, they are far worse than humans in demonstrating, ‘fluid intelligence’, or operating independently of rules, processing new information, or performing tasks without clear objectives. Futurists have prognosticated the replacement of humans with robots, powered by artificial intelligence, for at least six decades. ![]()
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