Making computers see the world the way we do
Have you heard about the new buzz term in analytics, “Deep Learning”? Although it might seem like some obscure, abstract addition to the already cluttered analytics space, Deep Learning is actually more of a combination of other analytics methodologies that tries to accomplish something better.
You might even be using some form of Deep Learning without realizing it every day through search engines, smart phones, or virtual assistants. So, what is it? How does it work? And most importantly—how can you use it to your benefit?
Let’s explore what Deep Learning is and introduce some practical ways you can leverage it within your organization.
Deep Learning and how it works
Deep Learning is a type of machine learning and AI that trains a computer to understand something the way a human would perceived it. It goes beyond strict and pre-defined methodologies and algorithms in analytics. With it, computers can learn how to do human-like tasks like speech/image recognition and language processing both organically and with greater accuracy.
Recently, advances in Deep Learning, including improved algorithms, new types of neural networks, availability of larger data sets, and distributed cloud architectures, have allowed the rapid proliferation of Deep Learning in everything from virtual assistants to call center automation.
How can I use Deep Learning?
1. Image recognition
Deep Learning can be used to analyze, understand, and sort images in a way that humans do naturally. For example, Google has been experimenting with Deep Learning and AI to understand images, detect faces and objects, and assign images to specific categories. They’ve even released an API for people to try out.
This could eventually be extended into sentiment analysis, where systems analyze images to figure out what people are feeling. For example, people can get customized recommendations at popular tourist destinations by simply sharing live data based on photos shared on Instagram by the current visitors. Based on the image, Deep Learning can even identify popular restaurants among different demographics and even based on different personalities.
2. Natural Language Processing
Although this is nothing new—neural networks have been used to process and analyze written language for many years—new applications of these networks are being explored in Deep Learning.
These applications include finding patterns in customer complaints/feedback or more accurately interpreting scanned text through optical text recognition (OCR) based on the surrounding sentences, topics, and writing style.
3. Search engines
Deep Learning can be used in combination with search engines and recommendation systems to predict complex relationships between your past customer preferences/searches and what they might want in the future. This goes well beyond something like Netflix where, for example, it sees that you watched Star Trek and based on the history, recommend other sci-fi shows.
Instead it could involve far more complex recommendations or searches, such as an online store seeing that you bought a certain hat and coming up with a list of recommendation clothing that match not just the hat but also your other purchases, search history, budget, personal style, and even other people’s preferences and new/upcoming styles.
4. Speech recognition
Business are already leveraging deep learning for speech recognition in the Internet of Things, smartphones, and virtual assistants to recognize differences in human speech and voice patterns. For example, Deep Learning allows the Apple HomePod and Google Home to differentiate between one user and another, or even between the sound from a television and the actual user.
Other cutting edge uses for Deep Learning and speech recognition are secure voice authorization and enhanced sentiment analysis. What if you could accurately match the tone and inflection in a customer’s voice with their probable mood and intent?