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AI has been empowering many of the world’s top technology solutions. Within e-commerce, AI is being used to make the industry increasingly customer-centric. This can be seen with sales forecasts, product recommendation engines, virtual personal assistants like Siri or Alexa, AI-powered chatbots, automated warehouses, etc.
The need for visual search in e-commerce
Large e-commerce companies offer catalogs with thousands of products and plenty of options. Customers, however, are becoming increasingly impatient during the buying process figuring out what they want to buy in the shortest possible time. This hence poses the question of how to make the search process short and seamless.
In an important use case, AI has enabled search engines to become smarter. This can be seen when using text as a search query, the search becomes more semantic and conversational. AI has also enabled enhanced voice search features. The latest popular feature being search via images.
AI and visual search: The tech
Visual search is a very recent trend, and this has been possible only due to the recent advancement in this technology. Visual search is built using Deep Neural Networks, a subset of machine learning. This in fact built as a replica of the neural networks in the human brain.  To put it simply, Deep Neural Networks make machines intelligent to gather and categorize information in the form of text, images or videos like humans do, using their biological neural network.
For example, To make a machine understand a sofa using deep learning, it is first shown pictures of thousand sofas. The algorithm reads and extracts features that can collectively classify a sofa such as a backrest, armrest, cushions, etc. After this, if a new image of a sofa is shown, the machine would be able to now tell if the image has a sofa or not.
Furthermore, if a complete picture of a living room is shown, the machine can individually identify different objects that it has been trained via deep neural networks such as rocking chair, coffee table, rugs, side table, etc. This technology is very adaptive – it recognizes a user’s search pattern so as to provide accurate purchase predictions.
Deep learning technology, providing accurate results can ensure that users find exactly what are searching for, in a short search span. This helps enhance user experiences, which in turn, leads to an increase in conversion rates. Neural networks and deep learning provide the best solutions to problems being faced in image recognition, speech recognition, and natural language processing.
 
Visual search has become one of the most successful technological innovations in e-commerce and retail, in turn, boosting the effectiveness on a global scale.
With the tremendous emphasis on digitization and the rising economy, this holds a strong promise.
Today, companies are looking for product differentiation through tech and visual search advancements offer just what they are looking for.