The Future of Human-Assisted Machine Learning

The Future of of Human-Assisted Learning

In today’s data-driven analytical world, there’s simply too much data for any human to crunch the numbers on. As a result, we’re seeing machine learning playing a significant role in the development of ever-more sophisticated analytics models through Amazon, IBM’s Watson, Microsoft’s Azure, and more.

Algorithms can help these systems to learn to draw better insights by improving their data collection and analysis over time. But what about things that still require a human touch?

In trying to analyze online conversation, a machine would have a hard time picking up on sarcasm. This is something that still requires some sort of human intervention (for now, at least). Similarly, the voice-fed questions and instructions that are said to Siri, Cortana and Google Now will invariably lead to confusion on the part of the machines, with responses like, “I didn’t understand that.”

But this week, Facebook announced that it is launching M, a virtual assistant that will add a distinctly human touch to voice commands through its Messenger app. Think of it like a combination of Siri and a concierge, as M will be a hybrid of artificial intelligence and human interaction.

While the feature will only be available to a few hundred people in the San Francisco area to begin, the machine learning backed by real people will become more sophisticated to the point where it can scale.

Think about this with respect to marketing automation. While CRM systems can trigger content or communication based on behaviors or activity, it’s pretty much all pre-scripted, isn’t it? There’s a distinct lack of human interaction with such scalable marketing efforts. But the reality is that people crave personalized, timely and relevant interactions because that’s what drives engagement and conversions.

While you can’t completely scale human interaction, is the future inextricably tied to more automation? Maybe it’s a hybrid of both.

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