We often face the dilemma to choose especially when we are going out shopping. Most of us have taken the advice of friends or family members when we are on a shopping spree. Visual chatbots are entering the ecommerce domain where technology has enabled Amazon Echo users to shoot the same question to an AI-powered chatbot when they are shopping online. The bot revolutions rely on visual cues like Amazon's look that will analyze photos of two different outfits, to select the best one based on different parameters.

The look may only appeal to users who are into fashion but missing on the chatbot space. In many occasions, most of us have had frustrating experiences interacting with chatbots. They do not always understand the context and cannot deal well with unpredictable speech scenarios which may lead to an unpleasant user experience.

The Premise of the Human Chatbot Interaction

Chatbots can only be successful if they are able to effectively communicate an issue with the user which the bot can understand. When communication is made by voice and text having undeniable limitations it might lead to inaccurate experiences which can be frustrating. If you have received unhelpful answers from a chatbot you are not the only one as research point 59% of people have thought that chatbots are slow to resolve problems. The connection between humans and bots is inherently flawed as people don't just communicate with words instead use inflection, visuals, body language. If you have used a pen and paper to sketch to describe something that words could not explain. Here visual chatbots could be the missing link to close the communication gap.

Visual Chatbots and their Adaptability

Chatbots with vision capabilities have become a possibility due to massive advances in deep learning and image recognition allowing AI to interpret different patterns with high accuracy improvising to tackle more complex visuals. The evolution process of visual chatbots would likely happen in detailed phases as below-

Serving an image through receiving a text is the simplest phase which is in motion currently when a bot receives an image request to serve up the correct image based on the requisite input. For instance, if someone is wishing to buy an electrical appliance says, 'Let me see the interior of the appliance and the working' prompting the bot to search an image database of images and would display the correct one. However, this implementation may come with significant computing power and bandwidth that large-scale image processing requires something every company cannot invest to deliver.

Bots will use image recognition techniques integrating along with text to return relevant outputs as text and images. For instance, if the user uploads the image of an appliance part, the bot recognizes it, to return with the name and price of that part. Additionally, it shares pictures of many other parts to tell the customer that there may be a requirement to buy those parts as well.

However, if the chatbot has to develop visual intelligence, there is a requirement to understand a tremendous amount of visual data. Something as simple as a request to check the interiors of an electric appliance requires that the bot sees it, recognize it to index appropriately for the user to find answers to his/her questions.

It becomes even more complex when the customer sends an image. For instance, to properly recognize any electronic component, the bot must be programmed to recognize it from a number of possible angles. The only way towards a solution is to create massive data sets enabling bots to learn from them. Another challenge that lies towards implementation lies in the time-consuming and labor-intensive work that comes with it. Workers annotate images to verify the annotations of others to program machines better understand visual inputs, among other tasks. When a bot has to receive and analyze images, to serve recommendations it has to recognize areas of intricacies to create an output and identify what repairs are needed. Eventually, chatbots will serve customers with live videos, helping and assisting them with their requirements.

At the moment visual chatbots are at a nascent stage capable to tackle simpler requests. With rapid technology advances, however, expectations are high to evolve from occasionally helpful text assistants to full visual interactors, efficiently capable to deliver customized advice for any occasions.