The Rise of Multimodal Models: How They Can Benefit Your Business

What is a multimodal model?

In multimodal deep learning, the term "modality" refers to 1 of the 5 human senses: seeing, hearing, tasting, smelling, or touching, and in this application, the reference is to data. Therefore, multimodal data involves information that can be directly translated from 1 or many of these 5 senses. For example, images or audio. Within multimodal learning, the data is unstructured, meaning it doesn't have structure or isn't available in a format that's easily read using one of the many machine learning tools.

Originally, in artificial intelligence and machine learning, data was restricted to an understandable format. With advances in deep neural networks, multimodal machine learning became better at understanding information and sorting different data formats to use in training and evaluating models. Consequently, a multimodal model can combine various types and modes of data to achieve more accurate results.

In multimodal learning, at least 2 sources of information are used. The individual features from each input modality is extracted, and the individual sets of extracted features are then combined and filtered using an aggregator to achieve the final result.

The extraction of features is accomplished using a neural network trained to identify specific parts of the data. For example, if the input data are images, the feature extractor may be trained to evaluate each image and determine the precise edges surrounding whatever the model is trained to note. The extraction of the individual features for the different data types is carried out independently.

Combining the multiple modalities for the different data types is carried out in a way that often gives weight to the data types, depending on how accurate the extraction was or the value applied to the data. Feature aggregation is used to produce the final result from among the different features, presenting the output from the multimodal model.

Examples of multimodal models

The most common applications of multimodal deep learning are image capturing, visual question answering, and caption or description generators. These AI content generators are trained to review datasets of images that have been previously labeled by experts. After evaluating the images (data type one) and labels (data type two), the multimodal machine learning model can be presented with a new, unseen image and generate a label for what the image contains. With better training, the labels generated by such a model will be more accurate.

Another example of a multimodal model is AI writing tools. These tools can be presented with a specific prompt and will generate an output matching that prompt. In the past, training these models generally required using multimodal data from within a specific field, such as medicine or engineering. However, recent models that can be applied to multiple fields have been developed.

The ability for AI models to be trained using one source of data and subsequently applied to a completely different type of data is one of the hallmarks of deep learning models. For example, a model trained on data from one organization can be applied to another organization's data via transfer learning. This process will minimize the cost of the second organization developing its own solution and can be used within a company for models applicable to various departments or teams.

Benefits of a multimodal approach

When using a multimodal model, a key benefit is the ability to automate a workflow. For example, if an organization has hundreds or thousands of images to label, doing so manually will be time-consuming and costly. However, utilizing a multimodal model that has been specifically trained on this task can help automatically label hundreds or thousands of images in a matter of seconds or minutes.

With a highly accurate model, the results could stand on their own. The outcomes of a less-accurate model could be evaluated to make manual adjustments, with the adjustments used in subsequent training for improved future results.

Taking advantage of multimodal data, this organization can reduce the workload for personnel, freeing them for more important tasks and subsequently reducing the costs associated with manual image labeling. If a specific machine learning solution doesn't already exist for the organization, building and training models will consume time and resources. Still, the resulting model will be applicable to future tasks and can be continuously improved to better serve the specific needs of the organization.

Despite the benefits of multimodal learning, this form of machine learning has its limitations. For example, newer machine learning and AI applications leverage information from within a specific time frame, such as before a certain year. As a result, this can lead to results with minimal representation or biased outcomes. Plus, there’s still so much that we have to learn about these tools, making it challenging to interpret or understand multimodal solutions.

How to use multimodal models at your business

There's no denying the overwhelmingly positive impact that AI has had in several fields around the world, and the advances in multimodal learning have contributed greatly to the successful use of machine learning.

As these tools and technologies become more cost-effective, the use of AI in marketing campaigns has become more of a reality. Businesses and organizations of all sizes can take advantage of technology that was unavailable only a few years ago.

From image capturing to natural language processing and automation, there are several ways to take advantage of multimodal learning.

However, multimodal models must be evaluated to ensure they're implemented effectively at your business. While some companies will clearly understand how multimodal data can be used to drive growth, others may find it difficult to determine where AI can be applied.

In today's digital and connected world, the amount of data businesses have access to has never been greater. This data is available in different formats and describes various business aspects. Because of these differences, multimodal machine learning has become extremely useful.

For example, a company may combine sales and customer data for training a model to predict future sales trends based on demographics gathered in real time from their website. Such a model may only exist for a company's specific product or customer base. Therefore, it may be beneficial for that company to coordinate with an external organization to develop a multimodal model that will apply to other products and customers.

From a marketing standpoint, understanding sales trends and the needs of customers can help provide marketing solutions that are better customized for specific audiences. Companies have access to customer data gathered from a number of sources, which isn't necessarily in the same format.

Again, multimodal learning can be used to extract the required features from different modalities to build an accurate picture and target specific customers based on real-time data gathered from a website or other source. As a result, this provides companies with a new marketing automation solution.

Use multimodal learning for success

Topics related to understanding and applying multimodal models are boundless, and the amount of information continues to increase. Using AI writing tools may be one of the most popular trends in machine learning, but the availability of this technology has the potential to create new solutions that can not only reduce costs associated with developing new marketing plans but can also help your marketing be more successful.

When it comes to AI and machine learning, there's no getting around the fact that it will continue to play an ever-increasing role in business. Whatever the size of your company, our collection of AI and automation tools can help drive business growth. Take advantage of automation to reduce costs and boost efficiency at your organization today.