This is one in a series of commentaries on quotes about AI offered in a recent article in Forbes by Rob Toews. Today’s quote is a recent one:
“For more than 250 years the fundamental drivers of economic growth have been technological innovations. The most important of these are what economists call general-purpose technologies — a category that includes the steam engine, electricity, and the internal combustion engine. The most important general-purpose technology of our era is artificial intelligence, particularly machine learning.”
which appears in an article in a 2018 article in The Harvard Business Review entitled The Business of Artificial Intelligence: what it can - and cannot - do for your organization. The article is by Erik Brynjolfsson and Andrew McAfee, who are co-directors of the MIT Initiative on the Digital Economy, at the Sloan School of Management.
If one looks up the Wikipedia entry on “general purpose technologies,” one finds that economists use this term to describe “technologies that can affect an entire economy (usually at a national or global level)[.” As an engineer, I find the term a bit jarring. Affecting national or global economies is undoubtedly a measure of a technologies success, rather than its generality. If one were to examine the history of technologies, they are almost always for a specific purpose. Then, if they are useful, people find a myriad of other uses for them.
Take, for instance, the first technology listed in the quote: the steam engine. While there are many ancient and historical uses of steam to turn this-and-that, the industrial origin of the steam engine is clearly the atmospheric engine, which arose from a very particular set of circumstances and a single purpose. In England, coal mines tended to fill up with water, necessitating pumping said water out. Having coal and water at hand, it made sense to burn coal to heat the water, use the resulting steam to raise a piston, then evacuate the steam into the atmosphere, let the piston fall, and using the rising-and-falling action to pump more water out of the mine. Channelling the excess water into canals led to the idea of floating the coal on the canals with horses pulling it along to market. The final step was, of course, to build a closed-circuit system like the atmospheric engine that you could put on the boat to haul the coal, and presto, the steam engine was born. And indeed, it did find general purpose.
But what of electricity and the internal combustion engine? Once again, as an engineer, I find these a jarring combination with the steam engine. Surely the proper set is the steam engine, the internal combustion engine (which was an evolution of steam engine concepts), and the electric motor. Each of these devices is a means of converting the potential energy in a fluid power source into mechanical motion. And surely any device that does that can become a general-purpose technology, in both the common and economic senses of that phrase.
Are AI and machine learning similar sorts of things? Are we now constructing general-purpose technologies of this sort, that convert some sort of “fluid” data into “intelligence” or “learning(s)”? This brings to mind the metaphor “data as the new oil,” which you can find discussed in Wired magazine, then later refuted in Forbes.
Computing has undoubtedly been a general-purpose technology. And Babbage did call his invention, the first general-purpose computer The Analytical Engine. But it is a fallacy to think of intelligence as mere computing unless we simply decide to make those two words synonyms. If we take a more considered approach to the nature of human intelligence, it only reduces to just being computation in a reduction to the absurd, where we say that since all physical processes can be simulated on a computer, they are a computer.
Think about it: this is like saying that because we can simulate all the physical processes in a rock, we can make computer programs that are rocks. This is even more absurd when one considers the realities of Complexity, as was discussed in the previous comment on a quote about the “singularity” by Von Neumann.
Computing is certainly like an engine, taking in data, and converting it into other data. But the conversion to real intelligence occurs when people examine data from computational engines.
Is AI a general-purpose technology? Is AI even a single thing? Is machine learning a general-purpose technology? Or is it also better described as an evolving bag of computational tricks?
I think the answer is that pseudointelligence (which I consider to be a much more useful term than AI) is a set of ever-evolving, beneficial technologies. But that we must realize they are not the engines of creation that human beings are, for very fundamental reasons.
Read more about those reasons in my book, Rage Inside the Machine.