10 Ways that Human-in-the-Loop Machine Learning is Used Today | Manning Publications

Creating great conversational experiences isn’t easy. As a result, most of them are less than impressive. To avoid lackluster results, consider an approach that incorporates humans-in-the-loop (HitL). With this approach, when a conversational application gets stuck, it can bring a human in to help. That human can ensure the end-user experience is fulfilled while also training the system so future occurrences don’t require intervention. This can dramatically accelerate time to launch whether you’re launching internal or customer-facing conversational applications or skills.

In a recent article by Manning Publications, the advantages of Human-in-the-Loop Machine Learning are clearly articulated: HitL is a game changer. From automated driving simulations with professional gamers to detecting cultural bias in data, all venues of AI become safer, more precise, and more inclusive.

“One of the most important questions in technology today is how can humans and machines work together to solve problems? More than 90% of applications that use Artificial Intelligence improve with human feedback. For example, autonomous vehicles get smarter the more that they observe human drivers; smart devices get smarter as they hear more voice commands; and search engines get smarter by observing which sites people actually click on for each search term. Human-in-the-Loop Machine Learning details the process for optimizing the interaction between Machine Learning algorithms and humans who create the data that powers those algorithms.”

Automation is only as good as the data, and in this powerful pattern, humans provide data to help train the bot. Users either feed the bot information for machine learning or script them in a particular way. Being able to incorporate the ability or skill to reach out to human colleagues (HitL) on different channels—whether it’s a call center, chat channels, text messages, or collaborative tools like Slack—to get the information that it needs improves the application in several ways. This article highlights a number of other HitL advantages, including:

“Avoiding bias, creating employment, augmenting rare data, maintaining human-level precision, incorporating subject matter experts, ensuring consistency & accuracy, making work easier, improving efficiency, providing transparency & accountability, and increasing safety.”

For better or worse, the hype surrounding conversational AI has set end-user expectations high. HitL can help you meet those expectations in a timely manner that matches user needs without deeply delaying launch timelines.

Read the full article from Manning Publications to learn more.

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