What Is a Visual Chatbot?

So, just what is a Visual Chatbot? Visual Chatbots are AI Chatbots that can recognize visual images and discuss them in detail. Put simply, they are chatbots that focus on image recognition and examination.

Often these chatbots are used in the diagnosing of different issues. You can imagine these chatbots are used quite often in the Medical and for a variety of industries where repairing a problem requires the examination of an image.

In this article, we cover everything you should know about Visual Chatbots, starting with:

  • What they are?
  • What sorts of benefits do they provide?
  • What are their drawbacks?

So, without further ado, let’s get to it.

What Is A Visual Chatbot?

The concept of a Visual Chatbot is still quite new. Software Developers knowledgeable about Artificial Intelligence are working hard to improve this technology because it is supposed to change the way chatbots work. But why is it so important?

First, let’s talk about the existing version of the Visual Chatbot so we can get a better idea about what it actually is. The difference between a Visual Chatbot and other chatbots is that it can see. If you upload, give the chatbot an image, and then ask questions related to that image, it can give you the right answers (that’s the goal at least).

The Visual Chatbot from Visualdialog.org
is one of the advanced Visual Chatbots. You can visit the website to get a better idea of how it works, but in case you don’t want to, we’re going to explain it here.

You can upload the image so the chatbot can know what you want to talk about. Only after a few seconds of pressing the upload button, the chatbot has analyzed the image and you can ask the questions. Remember when we said the technology is still quite new? This is exactly why you shouldn’t expect to get the right answers from the Chatbot. In case you’re willing to try, be warned that the answers can be quite stupid and hilarious.

As for our programmers, you should note that the Rudimentary approaches are mostly inaccurate. If you want an open-source code for Visual Chatbots, you can get it by visiting Github.com.

Search engines such as Google are using this technology for enhancing their search processes. If you don’t know it already, you can perform a search on Google by uploading an image. If you already knew it, now you know that Google uses Visual Bots for this process.

Although the Visual Chatbots are quite new, they’re making fast progress. It’s entirely possible that in a few years from now you can upload an image and get great answers to questions related to the image.

Now that you’re with the Visual Chatbot, let’s talk about its purpose in the workplace. Where exactly is it supposed to be used?

The purpose of Visual Chatbots is similar to other chatbots, to converse with a user. In this case however, the topic of conversation is an image presented by the user for the chatbot to examine.

Purpose of Visual Chatbot

The purpose of the Visual Chatbot in the workplace is to have conversations with the users. The bot is supposed to listen to or interpret what the users have to say and see their problems through imagery so it can solve the user’s problem in a better and more efficient manner than the current chatbots.

With the advancement of Visual Chatbot, bots will be able to solve the problems for the users in a significantly better manner. They’re supposed to provide benefits to both companies and the customers.

For companies, they mean a decline in labor cost, better customer experience, and more efficient work. For users, they’re going to benefit by getting their queries answered promptly and in an accurate manner.

These benefits look great for even one organization, but when you see it from a bigger perspective, it is supposed to make a big difference in the overall society.

By understanding the purpose of the Visual Chatbot, we’ve got a good idea of how much better they’re going to be than today’s chatbot. If you want to gain an even better understanding of the difference in the impact of both types of chatbots, read the next section.

What Makes Visual Chatbots Different?

Regular Chatbots

The current chatbot technology has decreased the cost of labor for companies by leaps and bounds. According to Jupiter Research chatbots are expected to make a big difference in the labor cost again. The research says chatbots are expected to decrease the labor cost by as much as $8 billion.

It is clear that the current chatbots have made an enormous impact on the labor cost, but it doesn’t mean that these chatbots come without drawbacks. Actually, the drawbacks are quite significant.

The biggest drawback by far is not so pleasant user experience. In the beginning, many experts thought that chatbots should be able to provide an efficient and pleasant user experience. But the reports show that this is certainly not the case. Because a lot of clients aren’t very satisfied with chatbots. The reason behind why current chatbots can’t satisfy the users is pretty obvious, they can’t see.

Here is why this is the problem. Whenever a communication occurs between a human and a chatbot, whether the communication is going to be a success or a fail depends highly on the ability of the customer to explain their problem accurately. With this, the ability of current chatbots to understand the complexity and nuance of the customers’ respect is limited as well. As if these two reasons aren’t already enough to make the experience more unpleasant, the vocabulary of current chatbots is quiet, limited, which can cause a good amount of trouble in communication.

For these 3 reasons, the user experience of current chatbots isn’t nearly as good as once thought by many experts. According to a survey taken by PointSource, 59% of the users say the reason why chatbots are not good at understanding humans is that they only understand the text. They don’t understand emotions and visual cues.

What the users say does make sense because we’re emotional and visual creatures. A huge part of our communication depends on body language, which is equal to visual cues. The big reason why social media networks with visual content such as YouTube, Instagram, etc are successful is that they have visual content. Without the visual content, these platforms wouldn’t have been successful.

Visual Chatbots

By reading the previous section, you can take a pretty good guess why Visual Chatbots are going to be a success. Because a huge part of the communication barrier between customers and current chatbots is that they can’t understand the visual language of human beings.

Since the advanced versions that are yet to come are supposed to solve this problem, it’ll certainly make an enormous impact on the user experience.

As for the labor cost, it is supposed to bring it down by a significant level as well. The reason is that because of the communication barrier, the number of cases where human employees have to intervene in a task to solve the problem is very high. Since Visual Chatbots are supposed to make the communication barrier much smaller, the number of cases where employees have to intervene is going to reduce significantly.

This will reduce the number of employees needed for assistance, which will cut the labor cost significantly. These are the differences between the impact of current chatbots vs Visual Chatbots on workplaces.

Now that we’ve covered this, let’s talk about the evolution of Visual Chatbot in detail. What exactly they’re supposed to do in the future, that they can’t do right now in order to achieve the results we’ve mentioned above.

Levi's Virtual Stylist is a great example of a working Visual Chatbot.

Evolution of Visual Chatbots

By reading the previous section it must have become obvious to you why the companies would adopt this new technology. In this section, we’ll take a look at the features that are supposed to make all those changes.

I don’t want to waste any more of your time than I have to, so let’s get to it.

Image to Image/Text

Bots are supposed to understand the requirement of the users by looking at the image. They can do it by analyzing the image and then give the data related to the image to the users.

Once the users have given the Visual Chatbot an image, they can ask any question related to the image and the chatbot will answer it.

Although the current version of the Visual Chatbot can perform this function, as you may remember they cannot do it very well. To achieve the results that we had mentioned previously, the modern version of the Visual Chatbots should be able to perform this task in a correct and efficient manner.

Here is what the ideal version of this feature looks like. The user has uploaded a photo of a Pitbull and wants to know the history of this breed, their size, features, genetic makeup, lifespan, and the cost of a puppy. The bot should be able to answer all these questions in a correct and efficient manner.

Image to Smart Image

This feature doesn’t only involve reading the data of any generic image and giving answers related to it. This one takes things to the next level.

The Visual Chatbots should be able to identify the condition of the object in the image. Here is a good example to illustrate what we mean.

Suppose you own a Honda Civic and you’ve been in a minor accident that has resulted in a damaged hood, minor scrapes and dents on the hood, and a broken headlight. Instead of taking your car to the company, you can simply take a few pictures of the damaged area from different angles so the damage is as clear as it can be in the image.

Once you’ve taken all the photos, upload them all so the Visual Chatbot can see the damage done. After a bit of time, the Visual Chatbot will see the extent of the damage and tell you the cost of repairing. If you’ve got insurance it’ll tell you whether the insurance policies involve taking care of the damage or not.

Companies are investing a huge amount of money and effort in this feature. Once this feature is implemented it’ll transform the current version of the workplace and bring it much closer to the one we had mentioned above.

Interactive Visual Conversation

This is the most advanced feature on the list. It involves the most realistic version of the conversation. Here is what we mean.

The bot should be able to interact with users through video. The bot should be able to see humans and it should use its own hands, facial expression, and other visual cues to interact with humans.

You may remember that the reason current bots have a bad user experience is that they can’t meet the visual appeal and the emotional appeal of human beings.

The features we’ve mentioned above such as facial expression, tone of voice, and other visual cues should not only meet the visual needs of a human but will meet those needs on a whole new level. The level that is supposed to take makes the interaction between bots and human beings much more interesting.

Of course, it doesn’t only possess the capacity to use body language for better interaction, but the purpose of the feature is more. The advanced Visual Chatbots will also be able to understand the subtle cues of human body language and will help them better understand what users want so they will be able to solve the problem.

This will dramatically increase the rate of problems solved by Visual Chatbots and it’ll also significantly improve the user’s experience.

These were the three features that are supposed to make Visual Chatbots become a huge success for the workplace. In the next section, we’re going to talk just a little bit about teaching the Visual Chatbots to reach the level we’ve mentioned above.

Teaching Visual Chatbots is like teaching any chatbot, it's a matter of presenting them with the material to learn from and showing them the meaning of the images presented.

Teaching Visual Chatbots

The advanced Visual Chatbots use deep learning to analyze the images in such ways that we have mentioned above. In order for the deep learning technology to perform these functions, it requires an extremely enormous amount of dataset.

To help you understand how enormous the amount of data there is in the dataset we’re talking about, we’ll use the example of the damaged car we had mentioned in the previous section.

To make an accurate guess of the damage done to only the headlights of the vehicle, the Visual Chatbot should have analyzed thousands of images of broken headlights from various angles, with various lighting, and the different amount of damage done to the headlight.

If the numbers of images that the Visual Chatbot has processed are not that high, the chances are it’s not going to give an accurate estimate of the repair cost of the headlight. By looking at this example now you have a better idea of just how much data the Visual Chatbot needs to process.

Hopefully, this has helped provide some information about why building a Visual Chatbot is such a complex and difficult task.

The bots are going to be quite expensive to create and it’s going to take a ton of time and effort to build them. With that said, they’re certainly worth the cost because the pros of Visual Chatbots will outweigh the cons by a ton.

Also, with time the price of the bots will increase significantly which makes it even better to create them.

We hope you’ve learned interesting insights about Visual Chatbots by reading this article.
If you think people you know will benefit from this article, feel free to share it.
If you have any questions or comments about the article we would love to hear from you below.

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