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Anyone whos worked in technology knows that certainbuzzwords rip through the industry every few years, sending executives into afever. Artificial intelligence, Big Data, Hadoop, and Web 2.0 (please,lets 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 thetechnologists out there?

If the hype doesnt match the actual industry impact, thenmany thousands of workers will have pursued a particular technology ordiscipline for nothing. But if the hype is justified, then folks can buildsatisfying careers (and make a lot of money). The stakes couldnt 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, LinkedIns 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.

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That outpaced robotics engineer (40 percent annual growthduring the same four-year period), datascientist (37 percent annual growth), full stack engineer (35 percentannual growth), and site reliability engineer (34 percent growth). (In order toarrive at its conclusions, LinkedIn crunched data from all of its publicprofiles over the past five years.)

Sounds pretty solid, right? Even so, the A.I. industry comeswith a relatively high bar to entry, which could restrict the pipeline oftalent for the next few years. Employers want A.I. experts skilled in machinelearning, deep learning, Python,natural language processing, and platforms suchas TensorFlow. Those are skills that take quite some time to learn, to putit mildly, and demand a pretty strong background in programming andmathematics.

Theres also the issue of company buy-in. Executives lovebuzzwords, but they often balk at the cost of spinning up the relatedtechnology. At this years The WallStreet Journals Future of Everything Festival, Arvind Krishna, IBMs seniorvice president of cloud and cognitive software, suggested that projects tend todie once companies realize theyll need to spend a lot of time prepping thenecessary datasets: And so you run out of patience along the way, because youspend your first year just collecting and cleansing the data.

Plus, existing A.I. initiatives have amixed track record so far. Ubers attempt to build a self-driving car platformhas hit some snags, toput it mildly; IBMs much-hyped Watson platform has failedto meet some hospitals expectations for successful healthcare dataanalysis; and some analysts and pundits think that even well-monetized projectssuch as Googles DeepMind haventeither scaled or commercialized.

Nonetheless, the future seems prettybright for artificial intelligence and machine-learning initiatives. Even ifsome high-profile projects crash and burn, its clear from the data thatcompanies are rapidly hiring various types of employees with A.I. skillsclusters. According to Burning Glass, which analyzes millions of job postingsfrom across the U.S., jobs that involve A.I. are projected to grow 40.1 percentover the next decade; the median salary for these positions is $105,007 (forthose 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 learningtools and techniques will likely become a part of your workflow over the nextseveral years. That means its 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 crashcourse,completewith 25 lessons and 40+ exercises, thats a good introduction to machinelearning concepts. HackerNoonalso offersan interesting breakdown of machine learning andartificialintelligence.

See the rest here:

Artificial Intelligence Job Demand Could Live Up to Hype - Dice Insights