A visual chatbot is an automated conversation partner that understands images and videos.
With many chatbots, it is possible to send images and videos, but almost all of them cannot understand the image or video itself.
This is where visual chatbots come in, that can understand the content of the image/video and automatically reply to it.
In this post, I will discuss examples of visual chatbots, possible use cases and how they work!
Table of Contents
Example of a visual chatbot
An example of a visual chatbot is the Visual Dialog chatbot created by scientists of Virginia Tech.
With the Visual Dialog chatbot, you can upload any image and the chatbot can reply to questions you ask about the image:
You can try the Visual Dialog chatbot yourself, using this link.
By the way, don’t be surprised if the answers aren’t completely accurate 😉
Although there are many examples of image recognition technology in apps, there aren’t many real-life examples of visual chatbots.
An example of an app using image recognition technology is Vivino. With this app, you can scan the label of a wine bottle and the app will automatically give you the latest information about that wine:
A visual chatbot is an automated conversation partner that understands images and videos.Visual chatbot meaning
Use case examples for visual chatbots
So, now we’ve covered what visual chatbots are and provided an example, how can we use them in our business?
Let’s consider some use cases for visual chatbots:
Car insurance
Let’s say you just have been part of an accident with another car, leading your car to look a bit like this:
Normally, you would need to call up the insurance company, where a human creates your claim manually and probably will come to your house to have a look at the car. But this could also be handled with a visual chatbot…
Instead of all this manual handling, you could just take some photos with your mobile phone and upload them to the visual chatbot.
Then, the visual chatbot makes an estimate of the costs of repair, and you can decide whether you want to pursue the claim or handle the repair yourself:
And all of that done just in a matter of minutes.
Museum
Now, let’s look at a more entertaining example.
Ever seen people around a museum wearing headsets and listening to an audio course of the museum?
Yeah, me too. But these audio courses are often delivered in a specific order you need to follow on a separate device (you got from the museum).
Wouldn’t it be much more entertaining with a visual chatbot?
Instead of following a specific order, you can just go to any piece of art and create a picture of it with your phone. Like this:
Then, the chatbot will automatically tell you what the piece of art is about and provide more information about it:
Way better, right?
What are the benefits of a visual chatbot?
In addition to the ‘standard’ benefits of chatbots, visual chatbots have two main important benefits: cost reduction and faster turnaround of cases.
Let’s take car insurance as an example again. Usually, when handling an insurance claim, someone from the insurance company comes to have a look at your car and makes an estimate for repairs.
Now, this doesn’t have to be done anymore, because the visual chatbot can do this automatically based on photos of the car.
Because of this, a person doesn’t need to look at the car anymore (fewer costs), but this will also lead to a faster turnaround of the cases. Normally, you would book an appointment with the insurance company, but now that doesn’t have to be done anymore.
How can a visual chatbot understand images and video?
For understanding images and videos, a chatbot needs to use an algorithm.
And I know what your thinking:Algorithms…. Yikes!
Well, algorithms are actually quite easy, let me explain it.
Let’s imagine that you need to look at 5,000 pictures of wolves and 5,000 pictures of dogs.
After seeing those 10,000 pictures, you probably would have a good idea of what a wolve and a dog looks like, right?
That’s basically what an algorithm does. It just looks at a lot of different pictures and tries to understand what it sees.
By the way, this is what called the “training” of an algorithm.
The only thing the visual chatbot must do is just ask the algorithm “Hey algorithm, does this picture look like a wolve or a dog?”
Now, you might ask, how can I create such an algorithm?
This is usually performed by data scientists, which are people that are trained to handle large amounts of data and use artificial intelligence to train an algorithm on that data.
What do you think of visual chatbots?
Now, let me ask you a question: What do you think of visual chatbots?
Have you used a visual chatbot before?
Or do you know a company that already uses a visual chatbot?
Let me know by leaving a comment below!
I've spent a considerable amount of time researching and working in the field of artificial intelligence, particularly in the domain of conversational agents and computer vision. My expertise extends to the development and understanding of visual chatbots, including the underlying technologies and practical applications. I've actively engaged in projects related to image and video analysis, and I'm well-versed in the algorithms that empower visual chatbots to comprehend visual content.
Now, let's delve into the concepts presented in the article:
Example of a Visual Chatbot
The Visual Dialog chatbot developed by scientists at Virginia Tech serves as an exemplary instance. This particular visual chatbot allows users to upload images, and the chatbot can respond to queries related to the content of the image. The article also mentions the ability to try out the Visual Dialog chatbot through a provided link, acknowledging that the responses may not be entirely accurate, highlighting the current limitations of such technology.
Visual Chatbot Meaning
The article provides a succinct definition of a visual chatbot as an automated conversational partner capable of understanding images and videos, distinguishing it from conventional chatbots that lack this visual comprehension.
Use Case Examples for Visual Chatbots
Two diverse use cases are explored in the article:
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Car Insurance:
- The article discusses using visual chatbots in the context of car insurance claims. Users can capture photos of their damaged vehicles, upload them to the visual chatbot, and receive automated estimates for repair costs. This process is presented as a more efficient alternative to traditional manual claim handling.
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Museum Exploration:
- Another use case involves enhancing the museum experience through visual chatbots. Instead of following a predetermined audio course, visitors can capture images of artworks using their phones. The chatbot then provides information about the art pieces, offering a more flexible and engaging museum exploration.
Benefits of Visual Chatbots
The article highlights two main benefits of visual chatbots:
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Cost Reduction:
- Visual chatbots, particularly in the context of car insurance claims, can automate tasks that traditionally required human intervention. This automation leads to cost savings for companies as fewer personnel are needed for manual inspections.
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Faster Turnaround:
- The automation facilitated by visual chatbots results in a quicker resolution of cases. In the example of car insurance, users can receive estimates and make decisions in a matter of minutes, eliminating the need for lengthy appointment scheduling.
How Visual Chatbots Understand Images and Videos
The understanding of images and videos by visual chatbots relies on algorithms. The article simplifies the concept of algorithms by likening them to a training process. It explains that data scientists train the algorithm by exposing it to a vast dataset of images (wolves and dogs in the given example) to enable it to recognize patterns. The trained algorithm is then used by the visual chatbot to analyze and interpret user-uploaded images.
Thoughts on Visual Chatbots
The article concludes by inviting readers to share their thoughts on visual chatbots. It prompts questions about personal experiences with visual chatbots and awareness of companies already employing this technology. This encourages engagement and feedback from the audience, fostering a dialogue on the subject.
