How AI Uses Feedback Loops to Learn From Its Mistakes

The best ideas were grown on the fertile ground of failure. And like the most innovative humans, the best AI learns from its mistakes. Put the two together, and you’re on track for AI-powered, human-centric success.

Long gone are the days of rules-based chatbots. Today, virtual agents are able to serve customers in manifold ways thanks to deep learning and NLU technology.

What sets this new generation of conversational AI models apart from others?

The ability to improve its accuracy using feedback loops.

Think of a conversational AI model as a copy of a human brain: It lives within what’s called an artificial neural network, consisting of several layers of AI that pass information along amongst each other. This information is based on input from the real world. Once it's been passed along between the layers, the model produces output.

Sometimes, there are inaccuracies in information flows between these layers.

A typical example of this would be an AI-based image recognition program mixing up a dog with a cat. And in conversational AI, your model might confuse intents.

In both cases, the more incoming data the model receives, the more opportunities for it to make mistakes — and to learn from them. This is where feedback loops, or backpropagation algorithms, come in. They identify inconsistencies and feed the corrected information back into the model as input.

By the time you put your AI model into practice with Ultimate, it has been trained with both industry-specific benchmark data and your very own historical data to provide the most efficient and personalized automated customer journey possible. And it’s gotten there by constantly adjusting its accuracy through backpropagation algorithms, or feedback loops.

Cover the essentials of customer support automation in our interactive learning hub.

But the true magic of automation doesn’t stop with your virtual agent’s launch date. Because the nature of your support is bound to change over time (think peak season or changing consumer habits due to Covid-19), your AI model can always be adjusted to grow even more accurate.

How?

By applying the logic of feedback loops, or learning from past mistakes, to continuously improve your AI model. Except with a special twist — one involving human empathy and industry expertise.

Here’s where your chatbot or automation manager comes in. As your AI model flags potential inaccuracies, or confusions, in our Training Center, this increasingly sought-after member of your support team is here to verify that new incoming messages are always accurately (re-)routed to existing intents.

They can also create novel intents based on newly occurring support topics — for example, by identifying customer sentiments or emotions and training your AI with real-time data to classify expressions and customize responses accordingly.

Deep learning is powering the revolution of automated customer support at lightning speed. And your human support staff holds the power to lead the way.