Artificial Intelligence

Artificial Intelligence Job Demand Could Live Up to Hype


Anyone who’s worked in technology knows that certain
buzzwords rip through the industry every few years, sending executives into a
fever. “Artificial intelligence,” “Big Data,” “Hadoop,” and “Web 2.0” (please,
let’s do our best to forget that last one) are just a few of the more notable.
But which ones will translate into actual opportunities and jobs for all the
technologists out there?

If the hype doesn’t match the actual industry impact, then
many thousands of workers will have pursued a particular technology or
discipline for nothing. But if the hype is justified, then folks can build
satisfying careers (and make a lot of money). The stakes couldn’t be higher.

As we head into 2020, one thing is pretty clear: Artificial intelligence (A.I.) seems like one of those much-hyped terms that might actually translate into a really robust sub-industry. For example, LinkedIn’s 2020 Emerging Jobs Report (PDF) puts “Artificial Intelligence Specialist” as its number-one emerging job, with 74 percent annual growth over the past four years.

That outpaced robotics engineer (40 percent annual growth
during the same four-year period), data
scientist
(37 percent annual growth), full stack engineer (35 percent
annual growth), and site reliability engineer (34 percent growth). (In order to
arrive at its conclusions, LinkedIn crunched data from all of its public
profiles over the past five years.)   

Sounds pretty solid, right? Even so, the A.I. industry comes
with a relatively high bar to entry, which could restrict the pipeline of
talent for the next few years. Employers want A.I. experts skilled in machine
learning, deep learning, Python,
natural language processing, and platforms such
as TensorFlow
. Those are skills that take quite some time to learn, to put
it mildly, and demand a pretty strong background in programming and
mathematics.

There’s also the issue of company buy-in. Executives love
buzzwords, but they often balk at the cost of spinning up the related
technology. At this year’s The Wall
Street Journal’s Future of Everything Festival, Arvind Krishna, IBM’s senior
vice president of cloud and cognitive software, suggested that projects tend to
die once companies realize they’ll need to spend a lot of time prepping the
necessary datasets: “And so you run out of patience along the way, because you
spend your first year just collecting and cleansing the data.” 

Plus, existing A.I. initiatives have a
mixed track record so far. Uber’s attempt to build a self-driving car platform
has hit some snags, to
put it mildly
; IBM’s much-hyped Watson platform has failed
to meet some hospitals’ expectations
for successful healthcare data
analysis; and some analysts and pundits think that even well-monetized projects
such as Google’s DeepMind haven’t
either scaled or commercialized
.

Nonetheless, the future seems pretty
bright for artificial intelligence and machine-learning initiatives. Even if
some high-profile projects crash and burn, it’s clear from the data that
companies are rapidly hiring various types of employees with A.I. skills
“clusters.” According to Burning Glass, which analyzes millions of job postings
from across the U.S., jobs that involve A.I. are projected to grow 40.1 percent
over the next decade; the median salary for these positions is $105,007 (for
those with a PhD, it drifts up to $112,300).

Positions associated with A.I. skills clusters include:

If you work in any of these roles, A.I. and machine learning
tools and techniques will likely become a part of your workflow over the next
several years. That means it’s important to learn as much as possible about A.I.
Fortunately, there are a lot of resources online that can help you out,
including a Google “crash
course,”
 complete
with 25 lessons and 40+ exercises, that’s a good introduction to machine
learning concepts.  Hacker
Noon
 also offers
an interesting breakdown of machine learning and artificial
intelligence
.



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