We’re
marching through months and years since last year’s AI awakening. We can’t fairly say that the shortcomings it
has are permanent, but, as of now, what are they?
First,
although computer applications have excelled at many games, such as chess,
where they are vastly better than any human ever, and checkers, which was
electronically solved 17 years ago, they have not done the same with
bridge. Per BBO Weekly News on
July 21st, Bill Gates said, correctly, that “bridge is one of the
last games in which the computer is not better.” Artificial intelligence progress has done
nothing, so far, to change that, and it is noteworthy that even in a closed
system with completely defined rules, objectives, and scoring, it has not been
able to take over.
Not only has
it not replaced huge numbers of jobs, but “77% Of Employees Report AI Has
Increased Workloads And Hampered Productivity, Study Finds” (Bryan Robinson, Forbes,
July 23rd). The effort, “in
partnership with The Upwork Research Institute, interviewed 2,500 global
C-suite executives, full-time employees and freelancers.” It found that “the optimistic expectations
about AI’s impact are not aligning with the reality faced by many employees,”
to the point where, in contrast with 96% of C-suite executives expecting AI to
boost productivity… 77% of employees using AI say it has added to their
workload and created challenges,” and has been “contributing to employee
burnout.” Also, 47% “of employees using
AI say they don’t know how to achieve the expected productivity gains their
employers expect, and 40% feel their company is asking too much of them when it
comes to AI.” This is what we used to
call a disconnect. The author
recommended employers get outside help with AI efforts and measuring productivity
differently, and workers to generally “embrace outside expertise.”
A similarly
negative view was the subject of “Machines and the meaning of work” (Bartleby, The
Economist, July 27th).
The pseudonymous writer cited a paper claiming that although “in theory,
machines can free up time for more interesting tasks; in practice, they seem to
have had the opposite effect.” Although
in health care, automation can allow more time with patients, in others, as
“the number of tasks that remain open to humans dwindles, hurting both the
variety of work and people’s understanding of the production process,” “work
becomes more routine, not less.” Overall,
“it matters whether new technologies are introduced in collaboration with
employees or imposed from above, and whether they enhance or sap their sense of
competence.”
Similarly,
Emma Goldberg, in the New York Times on August 3rd, asked
“Will A.I. Kill Meaningless Jobs?” If it
does, it would make workers happier in the short run, but it could also
contribute to “the hollowing out of the middle class.” Although the positions that AI could absorb
might be lacking in true significance, many “have traditionally opened up these
white-collar fields to people who need opportunities and training, serving as
accelerants for class mobility:
paralegals, secretaries, assistants.” These roles could be replaced by ones with
“lower pay, fewer opportunities to professionally ascend, and – even less
meaning.” Additionally, “while
technology will transform work, it can’t displace people’s complicated feelings
toward it.” So we don’t know – but
breaking even is not good enough for what is often predicted to be a
trillion-dollar industry.
Back to the
issue of perceived AI value is “A lack-of-demand problem” (Dan DeFrancesco, Insider
Today, August 8th). “A
chief marketing officer” may have been justified in expecting that the Google
AI tools it introduced would “be an easy win,” as “in the pantheon of
industries set to be upended by AI, marketing is somewhere near the top,” as
the technology could “supercharge a company’s marketing department in plenty of
ways,” such as by providing “personalized emails” and “determining where ads
should run.” Unfortunately, per the CMO, “it hasn’t yet,” as “one tool
disrupted its advertising strategy so much they stopped using it,” “another was
no better at the job than a human,” and one more “was only successful about 60%
of the time.” Similar claims appear here
from Morgan Stanley and “a pharma company.”
In all, “while it’s only fair to give the industry time to work out the
kinks, the bills aren’t going to slow down anytime soon.”
In the
meantime, per “What Teachers Told Me About A.I. in School” (Jessica Grose, The
New York Times, August 14th), AI is causing problems there, per
examples of middle school students, lacking “the background knowledge or…
intellectual stamina to question unlikely responses,” turning in assignments
including the likes of “the Christian prophet Moses got chocolate stains out of
T-shirts.” Teachers are describing
AI-based cheating as “rampant,” but are more concerned about students not
learning how to successfully struggle through challenging problems. Accordingly, they are “unconvinced of its
transformative properties and aware of its pitfalls,” and “only 6 percent of
American public school teachers think that A.I. tools produce more benefit than
harm.”
I do not know
how long these AI failings will continue.
With massive spending on the technology by its providers continuing,
they will be under increasing pressure to deliver useful and accurate
products. How customers react, and how
patient they will be, will eventually determine how successful artificial
intelligence, as a line of business, will be over the next several years. After some length of time, future promises will
no longer pacify those now dissatisfied.
When will we reach that point?