Machine learning adoption thwarted by lack of human skills Posted on : Dec 10 - 2018

New research shows the biggest barrier to machine learning adoption is a lack human skills (the irony!)

We’ve all heard the dire predictions about robots coming to steal our jobs. As technologies such as machine learning, AI and automation advance by the day, workplaces everywhere are being transformed; naturally, some people fear that they will become redundant — depending on their job, some may be right.  In this age of AI and ML, ambivalence is ripe; but something ironic has emerged: when it comes to advancing these cutting-edge technologies, the lack of human skills and knowledge is slowing innovation down.

In a new survey by Cloudera, the software firm, exploring the benefits and roadblocks of ML adoption across Europe, 51% of business leaders said that the skills shortage was holding them back from implementation. According to Cloudera, companies are eager to use ML — it’s second only to analytics as the key investment priority for businesses; ahead of other disciplines like IoT, artificial intelligence and data science.

“Although most IT buyers understand the benefits of machine learning, with 33% of respondents saying they have already seen tangible ROI from its use, many are still unsure about how to implement and how it will impact their businesses,” said Stephen Line, VP EMEA at Cloudera. “In what is still the early stages for many businesses in actually implementing ML, it’s unsurprising to learn that the skills gap and investment are key factors in preventing many companies from using it to improve efficiency and drive growth. That said, with the benefits of ML quite clear, the race is now on for businesses to overcome their barriers to deliver a better experience for their customers.”

A new profession

Similar to data science, ML is progressing in a distinctly different way to other job markets. Because ML rotates around gathering, collating and interpreting data, it traverses numerous disciplines; maths, statistics, and programming are all required. It’s difficult to write this in a job description let alone actually find it.

As you can imagine, ML is pretty complicated stuff, and it’s not something just any old computer engineer can grasp. ML requires cream of the crop computer scientists who can deal with large volumes of data at scale.

A natural intuition for maths is essential. Traditional software developers don’t need to be that great at maths thanks to the availability of math libraries and other functions that relieve them from doing equations the hard way. With ML, a developer needs to grasp complicated math such as linear algebra, calculus and gradient descent.

As we all know, today there’s a dearth of skills in all areas of STEM. Information Age recently reported how 94% of business leaders surveyed by OpsRamp are having a  “somewhat difficult” time trying to find candidates with the right technology and business skills to meet digital transformation goals. View More