What Machine Learning Can and Can't Do - The New Stack

As machine learning products continue to target the enterprise, they are diverging into two channels: those that are becoming increasingly meta in order to use machine learning itself to improve machine learning predictive capacity; and those that focus on becoming more granular by addressing specific problems facing specific verticals. And while the latest batch of machine learning products across both these channels may reduce some pain points for data science in the business environment, experts warn that machine learning can't solve two issues regardless of the predictive capacity of the new tools: Last year, new machine learning market entrants focused on speeding up processes around mapping the context that a machine learning algorithm would need to understand in order to predict needs in a given business situation. For example, if a voice translation machine learning product was listening in to a customer service call in order to more quickly help the call operator surface the appropriate solution-based content, the first job of the machine learning product would be to create an ontology that understands the customer call context: things like product codes, industry-specific language, brand items and other niche vocabulary. Products like MindMeld and MonkeyLearn built automatic ontology-creators so the resulting machine learning algorithm had a higher degree of accuracy without the end user first having to enter a whole heap of business-specific data into the product to make it work. Others, like Lingo24, created their own specific vertically-based machine learning engines for industries like banking and IT so that their machine learning translation service could apply the right phrase model to the right situation.