Machine learning is not just for experts

by L&D28 Oct 2016
There was a time when machine learning – the science of getting computers to act without being explicitly programmed – was best left to the experts. However, changing times can mean changing skillsets for employees.

Also referred to as “deep learning”, it on the development of computer programs that can teach themselves to grow and change when exposed to new data. In the 21st century workforce, this skill is both relevant and can be of great value.

As Coursera puts it:

“In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it.”
So if you’re not using deep learning already, you should be, says Josh Schwartz, chief of engineering and data science at Chartbeat.

In an article published in the Harvard Business Review, Schwartz pointed to a keynote address this year by legendary Google engineer, Jeff Dean at a conference on web search and data mining.
“Dean was referring to the rapid increase in machine learning algorithms’ accuracy, driven by recent progress in deep learning, and the still untapped potential of these improved algorithms to change the world we live in and the products we build,” he said.

“But breakthroughs in deep learning aren’t the only reason this is a big moment for machine learning. Just as important is that over the last five years, machine learning has become far more accessible to non-experts, opening up access to a vast group of people.”

Schwartz said that for most software developers, there have historically been many barriers to entry in machine learning – most notably software libraries designed more for academic researchers than for software engineers as well as a lack of sufficient data.

“With massive increases in the data being generated and stored by many applications, though, the set of companies with data sets on which machine learning algorithms could be applied has significantly expanded,” he said.

Schwartz went on to outline how robotics and machine learning are changing business.
“The net effect of these new technologies is that a person interested in using machine learning need not understand the science of deep learning algorithms in order to experiment with cutting-edge techniques,” he explained.

“Tutorials and public code exist for applications as diverse as AI-driven art generation, language translation, and automated image captioning.”

He said use by non-experts creates even more demand for easier-to-use systems and uncovers new applications of machine learning, which inspires further research and development by experts.

“These new technologies affect who works in machine learning as well. When hiring into applied machine learning positions, exceptional quantitative skills are critical, but direct education in machine learning itself has become less important,” he said.

“In many ways, this change in accessibility mimics the progression we’ve seen in software development as a whole.”

Schwartz pointed out that over the last 50 years, software development has gradually migrated from “low-level” languages – highly technical languages that closely relate to a computer’s underlying architecture – to high-level languages with significantly lower barriers to entry.

“Similarly, software deployment has migrated from hosted machines and data centres to cloud-based services, with massive decreases in the time and capital required to deploy a new system,” he said.

“These changes have not simply made software developers more efficient; they have allowed a much broader set of people to develop software and start software companies.”

He said software “boot camps” now train working engineers in a matter of months, and start-ups can turn ideas into products “in a few development cycles”.

“All of that is not to say, of course, that there’s no place for experts — as in software engineering, untold amounts of scientific progress have yet to be made in machine learning,” Schwartz said.
“But for the first time in history it’s possible, for example, for a person with knowledge of programming but no machine learning experience to create in one afternoon a neural network that can read handwritten digits. Try it for yourself.”