The Role of Deep Learning in Search

Deep learning is a subset of machine learning and artificial intelligence. The best way to look at deep learning is to think of it as “human imitation,” as deep learning aims to copy the methods in which the human brain receives and interprets knowledge. Because deep learning is not linear, it requires a large number of datasets to analyze and interpret information accurately and adequately.

Deep learning has many advantages, one of them being that the learning process is unsupervised. Being unsupervised, the machines learn and build from themselves and not human programmers —unsupervised learning results in faster and more precise processing.

Deep Learning’s Prominent Role in Search

With deep learning algorithms, the benefits to a search engine are far and wide. Here is a list below to name a few:

• Higher quality and more relevant search results suited to the user
• The ability to search and analyze contents of images and videos (the same way a program crawls through text)
• Interpretation and serving results in different languages across the globe
• Reducing the chance of an empty search and feedback of “no results”

The key to an insight engine is to feed the users relevant results when and how they need it. Whether in French, Spanish, English, video, image, or a text document.

Before deep learning, the search for “images of a fast red sports car” had to be described with metadata with the help of a human to trigger any results. Based on layers of training data, deep neural networks fully understand colors, which types of cars are fast, and key identifiers of a sports car compared to a different kind of vehicle.

The Mindbreeze InSpire appliance implements deep learning to ensure our customers receive results to their queries in the most relevant format possible. These methods allow our platform to self-learn and continuously provide accurate and timely knowledge by refining and optimizing search results.

Feel free to contact our experts to learn more!