When discussing artificial intelligence, people use three terms interchangeably. Artificial intelligence (AI), machine learning (ML), and deep learning (DL) are often thrown into the same bucket with individuals pulling the terms out randomly.
There is a distinct difference in ML and DL, which are subsets of artificial intelligence. Each system handles data differently, with varying degrees of human interaction and assistance. Yet business can use both to query and analyze the ever-increasing amount of data available.
Companies are left wondering which approach is best as they see exponentially increasing data.
Difference between deep learning and machine learning
We discussed the difference between deep learning and machine learning before. However, a refresher course never hurts. Very simply, DL is taking ML to the next level and is the next front edge technology under AI.
Machine learning – in simplest terms, machine learning uses algorithms to separate data, recognize patterns, and then make predictions or decisions. Humans need to put the data into context, and data must be in a single format.
Deep learning – although beginning with the same algorithm infrastructure, deep learning can build complex layers of understanding based on a concept placed in the primary layer. The more data available, the more sophisticated the concept becomes. Unlike it’s parent, DL can handle different formats including images and videos.
Think of it this way, deep learning is the next generation of machine learning. The infrastructure begins with algorithms, but deep learning can take the analysis to another level. So what exactly can DL do?
Deep learnings capabilities
As of 2015, DL is capable of recognizing images better than the human eye. The technology has developed to the point where scientists are trying to trick the systems into false recognition by laying noise over the image. As the DL system interacts with these adversarial learning images, it is beginning to differentiate between real and false images.
Also, DL is getting closer to human ability to translate language than any other translation method. Although human ability surpasses DL, barely in some cases like French to English, DL is far better than traditional methods. This can be applied to chatbots and any other customer service related experience.
The biggest potential offered by deep learning is the capacity to review data without a data scientist. ML requires tagging and organization of data, and the system cannot handle multiple formats combined. While there are times when data scientists need to input data, DL can function without human interaction if there is sufficient data from which to learn.
Deep learning and business
The big question is can deep learning benefit business, and should a business invest in DL?
Since the technology is front line, ahead of anything else in the AI spectrum, development is costly. The technology has not reached a capacity that diminishes the cost of development. Similarly, there is a shortage of data scientists who are trained and capable of handling deep learning. Since the technology is cutting edge, there is a limited number of individuals who come ready trained.
However, despite the two large concerns for business regarding adaption of DL, there are two huge factors in favor of skipping ML if not already underway.
The first is DL’s ability to handle non-homogenous and untagged data. For companies who have left data warehouses in favor of data lakes, DL appears to be a ready solution instead of a data scientist spending hundreds of hours cleaning data.
The second is DL’s ability to handle mass amounts of data. The more data the better. As IoT continues to flood data systems, DL is becoming more beneficial. One point regarding data: if your business does not have much data, DL will do little beyond what ML and a data scientist can offer. It will be too much system for business needs.
Deep learning is still expensive and the most innovative technology available. If an enterprise does not have mass amounts of data or various forms of data, there is little point to the investment. However, keeping an eye on deep learning is essential as AI and business continue to move forward.