Artificial Intelligence at the End of 2017: Three Large Questions and Answers

The idea of nonhuman intellectual capability has been around since at least the 98 years ago that author Karel Capek introduced a now-familiar word in his play R.U.R. (Rossum’s Universal Robots).  Since then, it has often gone under the name “artificial intelligence.”  As I have written before, that has been a misnomer, as everything being accomplished in it had been simply algorithmic, or limited to situations where something can be programmed to execute an “if a happens, then do b” sequence, which is not what we mean by intelligence. 

Over the past few years, though, the field has become more prominent, and its developments have reached many ordinary consumers.  People have published articles about “machine learning,” which, according to one definition, “is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.”  That technology has been credited for, among other things, reaching world-class status in go, a game which, along with bridge, poker, and chess, the most skilled and motivated humans can spend a lifetime learning without coming close to comprehensively solving.  We have also seen a variety of non-fundamental but large incremental improvements in home management devices and children’s friendship-robot toys.  Classic questions, such as “Will Artificial Intelligence Become Conscious?” (Subhash Kak, Live Science, December 10) and “A.I. Will Transform the Economy.  But How Much, and How Soon?” (Steve Lohr, The New York Times, November 30) have returned to the press, and salient concerns about the latest playthings have been raised by, among others, MIT professor Sherry Turkle, after decades still the leading figure on the social side of technology, whose “Why these friendly robots can’t be good friends to our kids” (The Washington Post, December 7^th) was released almost simultaneously with “Should Children Form Emotional Bonds With Robots?” (Alexis C. Madrigal, The Atlantic, December). 

All of this, though, is preliminary to three higher-level questions.  Is artificial intelligence still algorithmic?  What effect, beyond the established factors of globalization and efficiency and what we have already been able to project about automation, will it have on American and world employment?  And based on where it is actually going, is it dangerous?

The first question’s answer is yes.  Getting computers to program themselves is revolutionary, and cutting out the slowest and least effective parts, the people, will prove to be a huge improvement, and has already got us unquestionably fundamental gains, with many more to follow in the next months, years, and decades.  It can also be so fast and complex that many such systems cannot explain their results in forms succinct enough for human understanding.  However, machine learning itself is still limited to computational procedures, with nothing else under the surface.

On the second issue, we have five different responses.  In the October 29^th Salon “Will the AI jobs revolution bring about human revolt, too?,” Kentaro Toyama saw more to artificial intelligence than actually there, and interpreted author Ray Kurzweil’s old Singularity prediction as meaning that “by 2045, computer intelligence will match or exceed human abilities in every way.”  While it may be true that even now a product can come up with “eerie, dream-like images that seem genuinely creative and uncomfortably human,” that does not mean that such things are truly either, and employment requiring authenticity there will not go away.  In ”Who’s afraid of the A.I. wolf?” in the same publication seven days later, Crystal Point gave us the opposite view, saying correctly that “scientists still haven’t pinpointed the actual brain processes that make up awareness, and philosophers have not reached a consensus on the nature of this baffling state.”  In the November 9^th Motley Fool, Chris Neiger went back to the first position in “Artificial Intelligence Is Already Common – and It’s About to Take Over,” naming a study claiming that 38% of American jobs could go away to automation by 2030, a valid prediction in any event, and that artificial intelligence could “be capable of performing any task currently done by humans,” if not up and running in all of them, by 2060.  Lohr’s article above presented both sides, and a set of specific timelines, with 25% and 75% chances of success polled from “hundreds of attendees at two well-regarded AI conferences,” turned up in “Human obsolescence” in The Economist’s December The World in 2018 issue.  A few of the capabilities rated included “fold laundry” (25^th percentile of probability 2018, 50^th in 2022, 75^th in 2031), “retail salesperson” (25^th in 2022, 50^th in 2029, and 75^th in 2048), ending with “full automation of labor” (25^th percentile 2071, 50^th percentile 2141, and 75^th percentile 2241).  Despite the conference connection these chances also include existing forms of automation, but are enough to tell us that even those in the field do not think any kind of “singularity” is likely within anything like 28 years. 

On the third question, it is still a real concern that such machines will program themselves with systems that take living beings to be unnecessary, redundant, or even detrimental to their purposes.  The movie The Terminator may now be 33 years old, but its premise of “autonomous goal-seeking programs” failing to incorporate basic human assumptions is as current as today’s news.  Those involved with artificial intelligence must take a line from another 1980s popular culture work, Donald Fagen’s song “I.G.Y.,” and ensure that if we have “a just machine to make big decisions,” it is “programmed by fellows with compassion and vision.”  That is true whatever the eventual effects of artificial intelligence.