Over the last few years, deep learning – a branch of AI inspired by the structure of the human brain – has seeped its way into the technology we use on a daily basis.
Facebook uses deep learning to generate sophisticated algorithms that can be applied to sharable data like text, pictures, and videos. Google uses it in its voice and image recognition algorithms.
Researchers at MIT use it to predict the future. Amazon uses it to improve their smart speaker, Alexa, and Microsoft is using it to create speech software for Skype that can easily translate languages.
Some of the most powerful tech companies in the world have been taking advantage of deep learning for a while now, but you don’t have to be Amazon or Microsoft to benefit from deep learning.
In fact, many businesses can use deep learning in combination with their own data as well as data scraped from the web to power their operations.
Get 12 More Practical Uses for AI and Deep Learning Here
How Does Deep Learning Work?
The term “deep learning” is relatively new, but the concept has been around for some time, especially in how it relates to AI, Big Data, and analytics. Deep learning is, in essence, machine perception (though it shouldn’t be confused with Machine Learning).
With deep learning, data is fed through neural networks in order to analyze and classify it, creating smarter algorithms. The more data a machine receives, the more it learns about how to process and apply that data.
Chris Nicholson, co-founder of skymind.io, describes it as a way machines can interpret sensory data the same way humans do:
“Two main ways we interpret things are by naming what we sense; e.g. we hear a sound as we say ourselves ‘That’s my daughter’s voice.’ Or we see a haze of photons and we say ‘That’s my mother’s face.’ If we don’t have names for things, we can still recognize similarities and dissimilarities. […] Now imagine that, with deep learning, you can classify, cluster or predict anything you have data about: images, video, sound, text and DNA, time series (touch, stock markets, economic tables, the weather).That is, anything that humans can sense and that our technology can digitize. You have multiplied your ability to analyze what’s happening in the world by many times. With deep learning, we are basically giving society the ability to behave much more intelligently, by accurately interpreting what’s happening in the world around us with software.”
He points out that in order for this process to work, machines need training. He adds that to train a neural network to learn the same way the human brain does, it needs feedback that tells it when it gets something right and when it gets it wrong. If an algorithm fails to recognize a face in an image, for example, it will need to adjust.
The more data that a machine receives and the more training it has to recognize the right input, the smarter (and more useful it will be).
Deep Learning and Structured Data
In order to train machines to use data more intelligently, they need a lot of it. More specifically, they need a lot of structured data.
Zeeshan Zia, Senior Data Scientist at Microsoft, describes the need for structured data to power deep learning machines as akin to the human brain processing structure:
“In the pictures that almost always fall on our retinas, near-by pixels are typically close to each other in value, with sudden edges separating different regions. This is structure! […] When you talk to your friends, it’s not random gobble-de-gook. [You’re] using commonly known sounds (corresponding to certain words) spaced by a natural range of blank space.”
In the same way that the human brain processes the natural structure of language, images, and sounds, deep learning allows a machine to make judgments based on structured data.
But the key is that it has to be structured. The more structured data a company has access to, the better the outcome will be.
How Businesses Can Use Deep Learning
What are those outcomes, exactly? As mentioned, there are several practical applications that businesses can use deep learning for, even if they’re not tech giants like Amazon.
A few practical applications for companies include things like chatbots, news aggregators, image or document tagging, and ecommerce recommendations.
Build a chatbot. A chatbot is a digital agent that responds (as humans do) to common questions. They can be particularly helpful for ecommerce companies looking to expand or improve on their customer service, as the chatbot can respond to customers through a website or even Facebook Messenger. Companies can provide excellent service without having to hire additional manpower to run their customer service operations.
Create a news aggregator. With advanced natural language processing techniques like deep learning (among others), you can filter out news stories based on sentiment and then present it back to the readers. This can help you become a thought-leader in your industry or, at the very least, keep you updated on what’s happening in your industry around the world.
Tag images and flag documents. Image tagging is one of the first real breakthroughs to come from deep learning. Unlike text, which can be easier to classify, images can be harder to understand for machines because they require more pixel data. Thankfully, deep learning is improving and companies can use image tagging for things like social media marketing or even finding and flagging PDF documents or other images internally.
Generate product recommendations. Recommendation systems are a near essential for ecommerce businesses, especially with the sheer number of products on the market. Online retailers can use deep learning to automatically populate accurate product recommendations, which can increase the purchasing power of your site.
Identify your brand (or competitors) online. Ditto Labs uses deep learning to identify company brands and logos in photos posted to social media. You can also use image recognition to find out who’s talking about you online. Indico, for example, uses it to identify positive or negative sentiments about their business as well as topics and other keywords.
While those things may seem like small potatoes compared to Facebook or Amazon, keep in mind that deep learning will give you an advantage over your competitors no matter what, even if you’re not one of the “big guys.”
What else can you do with deep learning? Here are 12 more examples
There is a lot to be said about the benefits of deep learning, and so far we’ve only scratched the surface of what it’s capable of. As machine learning and AI progress, deep learning will become more and more relevant in terms of its applications for business.
For those wanting to take advantage of deep learning to gain an edge over the competition, it’s important to consider a few things. First, you will need to have access to a lot of structured data (which you can get with web scraping techniques like ours).
Second, you will need to know what you can do with it. Deep learning has a lot of practical applications, so the sky is the limit. Figure out what you really need from your business – do you want to search for brands online or do you really need a product recommender? – so that you know what kind of deep learning process you need to engage in.