Utilizing multimodal ML in a fast-shifting world

multimodal ML

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AI and machine learning are both terms that we so freely use. But, what many do not understand is that there are so many sub-areas of these disciplines. And we use them all interchangeably, which would be misleading. ML, in itself, can be divided into supervised, unsupervised, semi-supervised, and reinforcement learning. Each one comes with its own set of characteristics and terms. Because of such vastness of, not only ML techniques but data itself, we recognize the almost fresh term multimodal ML (machine learning).

Multimodal machine learning will continue its rise to the top in importance and approach to modern data. As we all know, our surroundings are not just simple text or numerical data. It’s the visual, audio, and other modalities that display the complexity and uniqueness of the world.

Have you ever heard of multimodal ML?

Have no worries if this term hasn’t come up yet. We seem to encounter so many definitions that describe new or existing technological advances. It almost looks like each day brings some sort of a new concept to describe a breakthrough that changes the world and drives innovation. Multimodal might not be new in terms of what it consists of, but it encompasses multiple areas of ML working in cohesion. 

We experience the world multimodal. We have senses and feel the world around us through sound, visual aspects, smell, feel or touch. If ML and AI want to understand and mimic the real world, first such data needs to be optimally utilized. And here is where the multimodal ML comes in.

Multimodal machine learning is machine learning where the model is trained with data from multiple different modalities, such as text, image, video, and audio. All of these come in different forms and characteristics. Data is not the same, and as such, it needs a different approach in utilization and machine learning methods. What’s important here is that one part, for example, the image, could be a misleading event to the human perception. But when we add sound to it, or text, it will provide a clearer picture. 

Through multiple modal datasets, multimodal ML tries to connect information and relationships from these sources to train models that can comprehend the intricate observed environment. But, it’s not only about text, image, video, or sound. Modalities can also include heat sensors, depth sensors, 3D visual data, LiDAR, and more. 

Fast-paced world and ever-shifting business environments

Data nowadays isn’t just some spreadsheet. It’s text, videos, music, apps, heat maps, online activity, and so much more. The level of diversity is continuously rising. Ever-shifting environments, especially business ones, are presenting more and more challenges in how to handle them. How do you cover all of the possible modalities and still make sense of the data?

Look at your social media accounts and how many modalities you create! Imagine this on a bigger scale now. Imagine it in terms of one business or a whole community. The scope is momentous. And it’s always shifting. 
Businesses across industries change constantly. The number of media, devices, and software involved in making business decisions is growing. But so does the format in which data is created. Keeping up is challenging. So it’s no wonder new concepts and approaches are created. Simple approaches toward data are no longer sufficient. Unimodal is not enough, and multimodal ML is going to take the helm in a lot of industries. It will almost become unavoidable.

Challenges of the heterogeneous nature of multimodal data

It is obvious that multimodal ML comes with limitations or challenges. The biggest one is the heterogeneity of data derived from modality diversity. Of course, each modal will generate different forms of data, which is easily seen from modal characteristics. So, it is a whole process to get such data on the same page. 

Images or videos won’t have the same sort of data or information as text or just audio. So, here lies the challenge of how to extract data for further processing and predictions. Ultimately, it’s about using multiple methods of data analysis and preparation to harmonize features from different data sources and formats. 

Let’s do an overview of how multimodal learning derives from unimodal components.

multimodal ML

Image: Multimodal ML architecture

Multimodal architecture, by most definitions, consists of three main steps: encoding, fusion, and classification

Because of the heterogeneous nature of inputs and modalities, they need to be put on the same page in terms of data features. Each unimodal input is processed separately, for example, the audiovisual model has audio and visual inputs, so they are processed individually in the encoding process. However, information from each unimodal model needs to be fused in the fusion step. Here the features from each modality are combined. This is one of the most important steps in multimodal learning and it defines the efficiency of the model. In classification, the model accepts encoded fused data and starts training on it. After this step, the data or features are ready for making predictions. 

It does come with its set of core challenges

Even though multimodal Ml seems like a great breakthrough, it does come with challenges that need to be addressed. Capturing and analyzing data from such different modalities is not an easy task due to the diverse format and meaning. Combining, for example, image and audio data is not as simple. Image and audio will have different characteristics and different ways to analyze and draw data from them. 


Each modality’s data is represented differently. Combining different representations is tasking. It’s hard to get them to a common language or format so they can be analyzed together. Creating a model that can handle and understand all these diverse representations is a challenge. Another issue is how to deal with missing and incomplete data and noise in data. 


This challenge is in terms of translating one modality into another. It’s about converting one form of modality data to another modality. The differences in the structure, syntax, and semantics present difficulties in translating from the source to the target language modality. For example, translating speech to text. It’s not a simple task.


In alignment, the challenge is to identify the direct relations and similarities between data of different modalities. It’s about matching element associations in modalities but of the same event.


Multimodal fusion is the ability to join data from different modalities to make decisions or predictions. Here we have different types of fusion, early, late, and hybrid fusion. It depends on where and at what level fusion was done. As said above, this step and challenge define the success of multimodal ML.


This addresses the challenge of transferring knowledge and information between modalities. If you are working with a modality with insufficient data, it’s beneficial if you can transfer it from another modality to fill out missing values or data to get the complete picture. But if the data doesn’t align or can’t be filled in, this becomes rather difficult.

Where does the multimodal ML lead us?

It might not be easy to use multimodal ML to its perfect precision. After all, the world around us, as much as data, is complex. But with time, maybe multimodal ML could compete with the diversity of it all. It, ultimately, serves as a tool to improve the precision of machine learning methods by combining unimodal models and trying to make predictions based on multiple aspects of data. 

We’ve already seen the application of multimodal ML through automatic image description generation, generative AI for images, and more like that. If you look at the breakthrough and precision from a year ago, now these technologies have surpassed expectations and quality. Imagine what it could do in the next year or even sooner. The possibilities are endless and will lead to more efficient ML and AI in handling data and predictions.

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