The Business Guide to Machine Learning
Machine Learning (ML) algorithms are embedded in the fabric of much of the technology we use every day. ML innovations spanning computer vision, deep learning, natural language processing (NLP), and beyond are part of a larger revolution around practical artificial intelligence (AI). Not autonomous robots or sentient beings but an intelligence layer baked into our apps, software, and cloud services that combines AI algorithms and Big Data under the surface.
The trend is even more pronounced in business. ML is no longer solely used for specialized research projects undertaken by a team of data scientists. Enterprises now make use of ML to gain actionable business intelligence (BI) and predictive analytics from ever-increasing amounts of data. That’s why it’s more important than ever to be aware not solely of what ML is but also the most effective strategies in which to use it for tangible value.
Ted Dunning, Ph.D., is the Chief Application Architect at enterprise Hadoop vendor MapR, and co-author of two books on what he refers to as “Practical Machine Learning.” The Silicon Valley veteran has worked in the field for decades, watching the AI techniques and the space evolve to the point where advances in cognitive computing and the availability of open-source tools has truly brought ML to the mainstream. Dunning spoke to PCMag to cut through the jargon and explain what ML actually means, and impart some wisdom and best practices on how businesses can make the most of their ML investment.
The straight definition of ML is giving systems the ability to act and to iteratively learn and make adjustments, without any explicit programming. Dunning said ML is a branch of statistics but a branch that’s very practical. He stressed that, in a real-world business context, you need to be pragmatic and realistic with how you apply it. The core task of ML is to create a business process that’s repeatable, reliable, and executable.
“Machine learning isn’t about looking backwards at scientific data and trying to decide what conclusions are viable,” said Dunning. “It’s about looking forward, and asking what we can predict about the future and what will happen in various scenarios. When it comes down to doing business with this data, we’re talking about very limited situations where you want replicability.”
You can break down that basic idea into a number of different fields within ML, but Dunning pointed to two in particular on either end of the spectrum: deep learning and what he calls “cheap learning.” Deep learning is the more complicated concept.
“We wanted machine learning to go deeper. That’s the origin of the term,” said Dunning. “Over the past 10 or 15 years, techniques have been developed that actually do it. [Machine learning] used to require a lot of engineering work to make relationships in the data visible to algorithms, which, for a long time, weren’t as clever as we wanted them to be. You had to hand algorithms this palatable data on a plate, so we used to hand-code all these features that systems now do on their own.”
Deep learning is where much of the innovation around neural networks lies.
