What Is Deep Learning?

What is deep learning?

Deep learning is a subset of machine learning that relies on artificial neural networks and layers. It does so to classify raw data and assign meaning to it.

Let’s break that down, shall we?

Machine learning is a type of AI that relies on algorithms and statistical models to help computers learn from large databases, and make decisions or issue predictions without having been explicitly programmed to do so. Examples of machine learning include image or speech recognition programs, AI used to aid with medical diagnoses, or conversational AI.

Deep learning is also a classification and learning method, but as the name suggests, it goes a tad deeper.

Artificial neurons and layered communication

Most deep learning models are based on artificial neural networks (ANN), a series of connected “nodes” that resemble biological neurons. They process information by transmitting signals to each other. These artificial neurons are aggregated into layers, and that’s where the “deep” in deep learning comes from: the signal travels through all layers (there can be dozens of them), the machine gaining more understanding of the data each step of the way. Each layer uses the previous layer’s output as input: for instance, in face recognition applications, the first layer might identify pixels and edges, the second a nose and eyes, the third hair, the fourth a face, and so on.

The evolution of deep learning in recent years

The first works on deep learning models started back in the 1980s, but the technology remained dormant until only a few years ago, when two conditions were finally met: Big Data happened, and computers became powerful enough to process it. Throughout the 2010s, advances in deep learning AI grew exponentially.

Deep learning AI hit a very widely-publicized milestone  2012, when Google Brain learned the concept of “cat” after analyzing tens of millions of untagged YouTube screengrabs. It taught itself to recognize cats without having previously "known" the idea of what a cat is.

Today, deep learning powers many applications, from image and speech recognition (used by personal voice assistants like Siri or Alexa) to medical image analysis and diagnosis, self-driving cars, fraud detection or the conversational AI models that power today's automated customer support. In short, it is at the core of many of today’s most promising innovations.

How deep learning can level up your automated customer support

When deep learning is combined with Natural Language Processing (NLP), a type of AI that helps computers read and understand natural human language, it can do wonders for your support. From analyzing your automation potential to identifying your most common customer questions, to understanding and answering them in many different languages, the combination of NLP and deep learning holds the potential to give your customers exactly what they need, when they need it. It's what's behind newer generations of chatbots and automated ticketing software that can correctly identify and immediately solve simple customer queries within seconds. With AI now saving support teams hundreds of thousands of dollars of overhead costs per year, it's the technology forward-thinking, digital-first companies need to get behind to keep up.