Distinguishing AI, Machine Learning, and Deep Learning:
As advanced computing continues to become an increasingly prevalent part of our daily lives, so does the terminology surrounding this technology. Phrases like artificial intelligence, machine learning, and deep learning have made their way into people’s everyday vocabularies across the globe. While these terms are often used interchangeably, it is worth noting what these words mean and how they differ.
Commonly referred to as A.I., Artificial Intelligence is the broadest of the three terms. It refers to the use of computers to imitate human intelligence through the use of if-then rules, decision trees, and other forms of logic.
Machine Learning is a category of artificial intelligence in which we use statistical techniques to improve a model with training and experience. The idea is that we have an abundance of data which we give to our machines to allow them to learn on their own. Their behavior is governed and restricted by a few key rules or mathematical tools.
One of the most common ways this is done is by use of a neural network. A neural network, like the network of neurons that make up a brain, takes in a number of inputs from the world (numbers, text, images, etc.) and rewires and adjusts itself based on how closely its predictions reflect reality. For instance, we might train a neural network to detect Husky dogs, but our model will be thrown for quite the loop when we give it a picture of a Golden Retriever dogs. Our model would have to adjust, to be broad enough to capture all the inputs we want.
The subset of machine learning that uses layers of neural networks to make sense of mass amounts of data for the software to improve itself is called 'Deep Learning'. Generally speaking, the more data a machine learning program is exposed to, the more effective it becomes. An example of this would be the speech recognition software in smartphones. Voice to text existed for decades, but it has come a long way in a short time due to Deep Learning. The software uses hundreds of millions of voices to continually improve its ability to recognize speech patterns.
How will AI, Machine Learning and Deep Learning Affect the Retail Industry?
For SpotCrowd, Machine Learning provides a useful opportunity to detect what is happening in a video stream. In every piece of video there is a wealth of information we can mine and turn into actionable data. We can pick out behaviors, such as concealing a product, or items, such as a backpack or a hoodie, which may help us understand if a shoplifting is in progress. These insights of Deep Learning, combined with the power of the crowd, will help us build a robust behavioral detection model to tell us exactly how shoppers behave, how they interact with the store, and when they break the law.