The Potential of Multimodal AI

Experts defined “Modals” as the different human sensory techniques like language, text, touch, seeing, and listening. When considering AI, modality means different data types. Data types like text, image, audio, video, and others can be used as input.

So, unimodal will simply mean one data type as an input and output. Similarly, multimodal means multiple data types as input to the generative AI model. The model then produces results in different modalities corresponding to the input.

Within artificial intelligence, multimodal learning aims to enhance robots' ability to learn by using copious amounts of textual data along with other forms of information, sometimes referred to as sensory data, such as photos, videos, or audio recordings. This enables models to discover new associations and patterns between text descriptions and the corresponding pictures, videos, or audio.

The difference between multimodal and unimodal

The picture here clearly illustrates the difference between multimodal and unimodal.

We have established in our series now that gen AI models are great learners. They are trained on data, and they learn and produce results from the training. Multimodal AI models are no different. They are also fed data; they learn and then produce results.

The basic concept of multimodal learning is cross-modal learning. In this concept, the models are trained to learn the relationships between various modalities.

Multimodal AI systems are trained to recognize patterns in several data input formats. There are three main components to these systems:

In reality, a multimodal AI system is made up of numerous unimodal neural networks. The input module, which receives various kinds of data, is composed of these.

Fusion combines data from multiple encoders to ensure the multimodal model maximizes each modality's advantages. Various methods are used in fusion, including early fusion, hybrid fusion, late fusion, and cross-modal fusion. The output module is in charge of producing the ultimate result or forecast using the data combined and processed by the architecture's earlier phases. These differ substantially based on the first input.

The tech is in its nascent stage. Many challenges were faced during its implementation. Some of the disadvantages of multimodal AI are-

Since data from diverse sources will not always have the same formats, combining and syncing various types of data can be difficult. It might also be challenging and time-consuming to guarantee the smooth integration of several modalities while upholding constant data quality and temporal alignment throughout the processing pipeline.

Every modality has distinct features and ways of representing them. For instance, text may require word embeddings or large language models (LLMs). Images may require feature extraction techniques such as convolutional neural networks (CNNs). Sometimes multimodal AI makes the results a bit complex that are difficult to understand.

Since each modality adds a unique set of information, multimodal data is usually high-dimensional and lacks dimensionality reduction techniques. The dimensionality of the data increases substantially with the number of modalities. This poses problems for the scalability, memory needs, and computational complexity of the AI models and the algorithms they employ to handle data.

Research is still being done on creating efficient structures and fusion methods to integrate data from various modalities. Achieving the ideal balance between cross-modal interactions and modality-specific processing is a challenging endeavor that calls for thorough planning and extensive testing.

Labeled data covering several senses is frequently needed for multimodal AI data sets. Large-scale multimodal training dataset maintenance can be costly, and gathering and annotating data sets with a variety of modalities presents unique obstacles.

Multimodal AI has been more popular than unimodal AI. Here are a few advantages of multimodal AI-

Multimodal AI can improve understanding of any topic. It can achieve resilience by utilizing input from many senses. For instance, by combining text, image, and audio modalities, you can get a more thorough understanding of user sentiment when evaluating customer feedback on a product.

Multimodal AI can improve user experience (UX) by giving users more options for interacting with the system. Virtual assistants are some of the best examples of increased interactions and enhanced user experience. Whether they are voice assistants, gestures, or text, they are designed to increase accessibility and convenience.

Multimodal AI can withstand noise and variability in the input data better by combining information from several modalities. For instance, even in the event of a deteriorating audio signal or a speaker's mouth being partially concealed, a speech recognition system that integrates visual and auditory data can still identify speech.

By allowing the system to concentrate on the most pertinent facts from each modality, multimodal AI can assist in the more effective use of computing and data resources. For instance, integrating text, photos, and metadata can assist minimize the quantity of unnecessary data that needs to be processed in a system that evaluates social media posts for sentiment analysis.

Multimodal AI has different applications in many industries. Here are a few industries that are successfully implementing the multimodal AI -

Multimodal AI promotes improved patient results through tailored treatment plans, and quicker and more accurate diagnoses. Multimodal AI evaluates several data modalities, such as pictures, data, symptoms, background, and patient histories. This allows healthcare practitioners to make well-informed judgments more rapidly and effectively.

Multimodal AI can be used to forecast yields, monitor crop health, and improve agricultural techniques. Farmers may optimize irrigation and fertilizer application. This leads to increased crop yields and lower costs. Combining satellite imaging, weather data, and soil sensor data gives an estimation of soil health and farm health. In turn, providing a deeper understanding of crop health.

The retail sector targets the supply chain management, product recommendations, and customer experience personalization, through multimodal AI. The analysis of many data modalities, such as consumer behavior, preferences, and purchase history helps the retail sector in improving efficiency. Multimodal AI offers retailers significant insights that are leveraged to enhance operational efficiency and revenue growth.

Manufacturing companies can use multimodal AI to optimize their production processes. They are also helpful in providing better products to customers. By integrating different input and output modes, such as speech, vision, and movement, the manufacturing sector reaps the maximum benefit of multimodal AI. This is why multimodal AI is becoming increasingly popular in the industry.

The use of multimodal AI is not limited to these industries. Industries like entertainment, automotive, learning, education, and many more are the successful use cases of multimodal AI. Many other industries are working on multimodal AI.

It won’t be an overstatement that Multimodal AI systems are the future of AI. Its ability to optimize operations and streamline procedures will enable businesses to operate more successfully and productively. It will result in increased profitability and process growth. We must seize this technology's great potential and apply it to the good of society.

Multimodal systems help us understand the world more closely in a way closer to what humans do.