Concepts & Benefits of Multimodal AI

Multimodal AI deals with richer types of data, as opposed to “single-modal” AI that typically handles text only. Although text-only AI can accomplish many wonders, the text data format limits user interactions with AI. You can compare this to the difference between two people communicating via letters versus phone calls. The latter provides much more nuance and enables richer communication.

The Concepts of Multimodal AI

To understand what multimodal AI is, you should first consider its opposite: single-modal AI. Text-generation technology is a form of single-modal AI. You provide text input to the AI, and it responds with text output.

A grammar editing application is a good example of single-modal AI. If you send the string “Alice eat an apple every day” to the app, it might respond with “The grammar is incorrect. The correct sentence should be ‘Alice eats an apple every day.’” Here, you can see that the single-modal AI application takes a text input and returns a text output. The input data type is singular: text.

Now, compare this to multimodal AI, which can process more than one type of data, such as text and images. You can send a text to a multimodal AI application without issue, and you can also send an image — both are acceptable. The multimodal AI application can receive and process multiple types of data. That’s the key difference.

An adult content detector app serves as an example of multimodal AI. If you send the text “Bob hugs the woman tightly and kisses her aggressively” to the application, it might return an answer like “80% likelihood of adult content.” However, you could also send a nude picture to the same application, and it might respond with “96% likelihood of adult content.” This application demonstrates flexibility in receiving and processing different types of input.

Benefits of Multimodal AI

The benefits of multimodal AI are clear and many. Multimodal AI applications have more natural and intuitive experiences. Suppose you build a translator application. If it accepts only text inputs, the app is still useful. You can type, “Surveillez Votre Tête” and then ask the app to identify the sentence’s meaning. The application can send you the answer: “Watch for Your Head”. That’s very nice.

But imagine you’re in a station and see a sign with the same sentence. What would you do? You could type the sentence into your phone and send it to the app. However, that’s not intuitive and takes time. What if the translator could accept other types of input, like images? You could simply take a picture of the sign and send it to the application. The app could then extract the text from the image before translating it for you. This experience is much more intuitive and natural.

Sometimes information is better presented in forms other than text. As the saying goes: “A picture is worth a thousand words.” Although some things are best conveyed through text, others are better communicated via audio or images. This is why multimodal AI often works better than single-modal AI.

Moreover, multimodal AI enhances accessibility for users of all backgrounds and abilities. Some individuals might find typing large amounts of text on a mobile phone easy, whereas others might find it more challenging and prefer speech input instead. Similarly, although reading extensive text might be straightforward for some, others might find it easier to receive information in audio form. By accommodating different preferences and needs, multimodal AI ensures a more inclusive experience for everyone.