Deep learning is one of the most advanced forms of machine learning, and is showing new developments in many industries. In this article, we'll explain the concept and give some examples of the latest and greatest ways it's being used.
What is deep learning?
There have been many attempts at creating a definition of deep learning.
As we've explained in the past, machine learning can be considered as a sort of offspring of artificial intelligence. In the same way, you can view deep learning as a further evaluated type of machine learning.
According to Wikipedia: Deep learning (also known as deep structured learning) is part of a broader family of machine learning methods based on artificial neural networks with representation learning. Learning can be supervised, semi-supervised or unsupervised.
While that definition does give us some clues on what we are looking at, it deserves an explanation of some of the terms used.
Artificial neural networks (ANNs) are computerized networks that mimic the behavior of biological communication nodes. What makes biological neural networks different from other artificial networks is that they are dynamic and analog. That not only makes them more flexible, but it also makes them harder to mimic in an artificial neural network.
Representation learning or feature learning is a set of techniques that allows a system to automatically discover the representations needed for feature detection or classification from raw data. In other words, representation learning is a way to extract features from unlabeled data by training a neural network
How is deep learning more advanced?
Basic machine learning methods are becoming better at what they were designed for at an impressive speed. But they still need human guidance from time to time. For example, when users notice that the algorithm has accepted a false statement as true. In such a case, the predictions made by the algorithm become worthless and the situation needs to be corrected.
Deep learning uses multiple layers which allows an algorithm to determine on its own if a prediction is accurate or not. As we all know, you can sometimes reach an accurate conclusion based on false facts. A deep learning model will typically be designed to analyze data with a logic structure and do that in a way that's very similar to how a human would draw conclusions. This layered approach results in a method that is far more capable of self-regulated learning, much like the human brain.
The obvious warning here is that not every human brain is capable of following the rules of logic and while we perfect the mimicry, we may introduce the same weaknesses that exist in biological brains. Of course, deep learning machines are capable of processing a lot more input than humans can at this point, which is why big data and deep learning often go hand in hand.
Examples of deep learning
Machine learning and, more specifically, deep learning already have proven their worth in some use cases and we can expect more improvements in these fields.
Traffic analysis: Predictions about which roads and motorways are acting as a bottleneck and how the flow can be optimized with a minimum of investments. For example, whether it will prove to be useful to add an extra lane to that highway or whether it will just create the same problem a few miles further ahead.
Transportation automation: In transport, the shortest route is not always the fastest. A delivery route can be optimized by time of arrival at certain delivery addresses, which is something that can be done by deep learning.
Finding cures: Deep learning neural networks can help in structuring and speeding up drug design. Researchers have enhanced deep learning for drug discovery by combining data from a variety of sources.
Market analysis: Combining machine learning with your data can provide insight into which leads prove to give you the highest success rate. However, given that you need a relatively big dataset, this may not be interesting for smaller organizations lest it may lead to self-fulfilling prophecies.
Speech recognition: Apps that listen to voice commands can learn to understand their user better over time. This can help to overcome the returning annoyance about voice assistants that misunderstand or not understand the user at all.
Gesture recognition: One of the latest additions in the area of machine learning deals with recognizing gestures. The signals that are emitted from sensors are able to detect emotions by energy, time delay, and frequency shift.
Deepfakes: For good or bad, further analysis of facial expressions and voice patterns can provide the data for the next step in creating more convincing deepfakes. By better understanding human behavior, it will become easier to mimic and provide more convincing results.
Smartphone cameras: These small cameras have to make up for the limitations set by their size in order to come close to the picture quality made by dedicated cameras. Machine learning algorithms do several things to improve and enhance the smartphone’s picture quality.
Targeted advertising: To minimize the number of advertisements the public have to watch, and to optimize the effectiveness of those advertisements, deep learning can be used to provide targeted advertising and make sure the aim is at the most suitable demographic for your product.
These are just some examples. You can probably come up with more if you look around you and see how software has taken over a lot of tasks that required human brains in the past.
The use of machine learning has also made things possible that were impossible before. For example, Google built a system to guard the rainforest. The company built a solution based on an open source platform for machine learning that uses audio to detect sounds of chainsaws and logging trucks to understand if any if an illegal activity is occurring. The machine learning solution takes into account various artificial intelligence techniques to ensure it is correctly detecting any destruction taking place.
The cybersecurity industry
We've already talked at length in another blog about how artificial intelligence and machine learning may impact cybersecurity. Some of these changes are already taking form and others are well on their way to being developed, but as we move forward there are bound to be changes. Especially in an industry that is involved in an arms race that entices both sides to stay one step ahead of the other.