Real Skills You’ll Need for A.I. and Machine Learning

Real Skills You'll Need for A.I. and Machine Learning

Find out what you're worth. Discover skills to earn more. Apply for jobs. All with the Dice Careers App.

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.

Share it:
Share it:

[Social9_Share class=”s9-widget-wrapper”]

Leave a Reply

Your email address will not be published. Required fields are marked *

This site uses Akismet to reduce spam. Learn how your comment data is processed.

You Might Be Interested In

5 challenges every multicloud strategy must address

17 Dec, 2020

Companies have been moving data, applications, and development work to the cloud in greater numbers for the past several years …

Read more

Will Big Data Lead to Too Much Control

9 Jul, 2016

Much of the talk surrounding Big Data relates to its benefits, and to be sure, Big Data has the potential …

Read more

Compute to data: using blockchain to decentralize data science and AI with the Ocean Protocol

11 Mar, 2021

AI and its machine learning algorithms need data to work. By now, that’s a known fact. It’s not that algorithms …

Read more

Do You Want to Share Your Story?

Bring your insights on Data, Visualization, Innovation or Business Agility to our community. Let them learn from your experience.

Get the 3 STEPS

To Drive Analytics Adoption
And manage change

3-steps-to-drive-analytics-adoption

Get Access to Event Discounts

Switch your 7wData account from Subscriber to Event Discount Member by clicking the button below and get access to event discounts. Learn & Grow together with us in a more profitable way!

Get Access to Event Discounts

Create a 7wData account and get access to event discounts. Learn & Grow together with us in a more profitable way!

Don't miss Out!

Stay in touch and receive in depth articles, guides, news & commentary of all things data.