Multimodal Learning: Benefits & 3 Real-World Examples in 2024
Multimodal AI, or multimodal learning, is a rising trend and has the potential to reshape the AI landscape. And even though the concept is new, it is growing as business leaders are realizing its benefits.
However, to avoid premature investments into multimodal learning, we have curated this article so adopters can first familiarize themselves with the technology, its benefits, real-world examples, and implications.
What is multimodal learning?
Multimodal learning for AI is an emerging field that enables the AI/ML model to learn from and process multiple modes and types of data (image, text, audio, video) rather than just one.
In simple terms, it means learning through different modes, whereby the different data types are combined to train the model. This expands the model’s capabilities and improves its accuracy.
Multimodal vs Unimodal
All traditional AI models are unimodal since they are developed for and required to perform a single task. For instance, a facial recognition system is provided with a single input, such as an image of a person it analyzes and compares with other images to find a match.
A doctor does not provide a full diagnosis until he/she has analyzed all available data, such as medical reports, patient symptoms, patient history, etc. Similarly, the output of a unimodal system fed with a single type of data will be limited.
Using a variety of data expands the horizon of the AI system.
What are the benefits of multimodal learning?
There are two key benefits of multimodal learning for AI/ML.
1. Improved capabilities
Multimodal learning for AI/ML expands the capabilities of a model. A multimodal AI system analyzes many types of data, giving it a wider understanding of the task. It makes the AI/ML model more human-like.
For instance, a smart assistant trained through multimodal learning can use imagery data, audio data, pricing information, purchasing history, and even video data to offer more personalized product suggestions.
2. Improved accuracy
Multimodal learning can also improve the accuracy of an AI model. For instance, the only way to identify an apple is not by its image or their vision alone, for they can also identify it via the sound of it being bitten or through its smell.
Similarly, when an AI model is shown an image of a dog, and it combines it with audio data of a dog barking, it can re-assure itself that this image is, indeed, of a dog.
What are some real-world examples and applications of multimodal learning?
This section highlights some real-world examples of how multimodal AI can be used in your business.
Meta’s project CAIRaoke
Meta, Facebook’s parent company, claims to be working on a digital assistant project based on multimodal AI, which can interact with a person like a human. The assistant is planned to be able to turn images into text and text into images.
For instance, if a customer writes, “I want to purchase a blue polo shirt; show me some blue polo shirts,” the model will be able to show some images of blue polo shirts.
Google’s video-to-text research
Google’s recent study claims to have developed a multimodal system that can predict the next dialogues in a video clip.
The model successfully predicted the next dialogue line that would be spoken in a tutorial video on assembling an electric saw (See image below).
Automated translator for Japanese comics
Scientists and researchers at Yahoo! Japan, the University of Tokyo, and the machine translation company Mantra developed a prototype of a multimodal system that can translate comic book text from speech bubbles which require an understanding of the context to be translated. The system was developed to translate Japanese comics. It can also identify the gender of the speaking character in the comic.
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