The world of artificial intelligence (AI) is evolving so rapidly that it’s already splintering into subsets.
To the layman, these subsets all seem like the same thing, and thus, we hear many terms like deep neural networks and machine learning used interchangeably.
But is this really the case?
Actually, it isn’t the case at all. And if these new technologies are to become a prominent part of our world – as most experts predict – then we should learn at least the basics of what they are and how they will contribute to society.
Comparing deep neural networks to machine learning
For starters, deep neural networks are a subset of machine learning – and machine learning is a subset of AI.
A deep neural network is more complex, sophisticated, and intelligent than a machine learning algorithm. It is typically more focused and has a more precise objective.
Deep neural networks provide valuable data for experts like data scientists and statisticians. One of their primary functions is to increase the accuracy of a given machine learning model. You can think of machine language as having the ability to generate images, whereas a neural network knows how to create art.
More about neural networks
In their simplest form, neural networks represent a level of complexity. Common vernacular is that it becomes a deep neural network when it contains at least two layers.
These deep nets evaluate data in complicated ways and utilize very sophisticated mathematical models. For non-techies to wrap their heads around the concept of a deep neural network, it is usually best to address how it evolved to its present state – as a few notable systems came along first.
As we start with the AI world, we realize that machine learning was the first level of refinement. We had a framework of automation using algorithms to improve its ability to make predictions on this level. As time passes, this model learns from its bad predictions and makes statistical adjustments to make fewer errors.
Artificial neural networks focused more on the learning function of creating more accurate models. These networks employ a hidden layer where data inputs are stored and evaluated to measure how significant they are to the output. It is this hidden layer where info about the importance of each input is stored, from which it will make associations among the importance of input combinations.
So when a second hidden layer is added to the process, it becomes a deep neural network, as we defined earlier. These additional layers improve the performance of a given model, as every node in these hidden layers is making associations and evaluating its input relative to the output.
Common sense tells us that adding more and more of these hidden layers will significantly improve the outcome’s accuracy. Of course, the overall complexity of the system will also increase.
An umbrella term often used to describe these deep multi-layered neural networks is ‘deep learning.’
While most everyone acknowledges that deep learning offers a lot of potential, it has not been utilized in recent years because of its two usability problems.
Deep learning requires an enormous amount of data – although there are a few exceptions to this rule. For instance, Tesla’s autonomous driving software must have several million images and video hours to function.
And deep learning must also have massive computing power. Today, the emergence and existence of high-performance GPUs and cloud computing infrastructure are slowly resolving this issue.
Deep learning is becoming more of a reality with each passing day. This is thanks to a field of deep learning called transfer learning, which uses pre-trained models. Experts see transfer learning as the remedy for large training datasets needed to produce meaningful results with these complex multi-layered neural networks.