Real Skills You’ll Need for A.I. and Machine Learning
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William Terdoslavich has spent a career in and out of tech journalism, having written for InformationWeek, The Hubbs.com, Computer Reseller News, Computer Systems News and Mobile Computing and Communications. Technology will always change. Human nature remains the same, usually crazy.
Ever since research scientists coined the term ‘artificial intelligence’ more than sixty years ago, the idea of a self-thinking computer has occupied a special place in the public consciousness. But now companies seem to have come around to the idea that, with enough technology and talent, A.I. can become an actual product.
Those firms include Google, IBM, Apple, Facebook, and Infosys. And they’re all fishing in the same talent pool for technology professionals who can build a workable A.I. platform. Such an unusual endeavor demands unusual skill-sets, making it difficult to land the right candidates for the job.
A.I. programmers have to come from somewhere. Perhaps unsurprisingly, the typical starting point is college.
“As someone who teaches an undergraduate A.I. course, I typically encounter students with no explicit prior A.I. programming experience,” said Jim Boerkoel, assistant professor of computer science at Harvey Mudd College. “Increasingly, students will have had some prior experience developing A.I. solutions as part of a summer internship or research program.”
While an understanding of A.I. and Machine Learning is becoming more commonplace, “there is still a pronounced shortage of talent,” added Gary Kazantsev, head of Machine Learning at Bloomberg, also speaking via e-mail. “In fact, it is getting worse as more and more enterprises form their own A.I. groups and make A.I. part of their corporate strategy.”
If people with sufficient A.I. experience cannot be found, there are other ways to fill the skill gap. “[We] expect everyone to do a lot of on-the-job learning. It is relatively uncommon to find candidates who have years of experience with machine learning problems, though it would be great if we could,” said Joel Dodge, a software engineer at Infer, via email.
Another hiring approach is to take a specialist in one field and teach them the skills for another, said Abdul Razack, senior VP and head of platforms at Infosys. That might mean taking a statistical programmer and training them in data strategy, or teaching more statistics to someone skilled in data processing.
Some skillsets do undergird a useful foundation for additional training in A.I. “The best way to break in to A.I. is to just get your feet wet and to learn… A number of our engineers took Andrew Ng’s Coursera course on machine learning before starting here, and this course is a truly great introduction to the field,” Dodge said.
“Mathematical background—solid grasp of probability, statistics, linear algebra, mathematical optimization—is crucial for those who wish to develop their own algorithms or modify existing ones to fit specific purposes and constraints,” Kazantsev said.
Skill sets aside, programming for A.I. requires a change in conceptual thinking. “The old paradigm was you knew the problem you had to solve… and you threw technology and skilled people at it,” Razack said.
“When looking at applying artificial intelligence to certain scenarios… there is this notion of problem finding,” he continued. This deductive skill is very much in demand, and exposes another facet of A.I. design: the first iteration of an artificially intelligent platform will probably get a lot of things wrong. An A.I. program has to “learn” its tasks through multiple iterations over time, all the while tapping into a large dataset for its information.
“For predictive apps, we also want the finished product to be ‘good enough’ at the predictive problem it is solving.
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