Multimodal AI Models: The Latest Craze in the AI Domain

With technology’s rapid advancement, artificial intelligence (AI) has become a game-changing factor. It’s transforming everything, from how we communicate and how we work to business management. The AI industry is experiencing an explosion, with certain AI trends at the forefront, one of the most popular ones  — multimodal AI models.

Table of Contents

• What Are Multimodal AI Models?
• Industries Benefiting from Multimodal AI Models
• Key Components of Multimodal Models
• Example of Multimodal AI Model in Action: Google’s MUM System
• Multimodal AI Models are the Future and the Future Is Now

What Are Multimodal AI Models?

Multimodal AI models are a form of artificial intelligence that combines two or more different data types — such as text, image, and voice — as input for better understanding and performance.

Consider a machine learning model used in AI applications that can understand both spoken or written language and identify objects in an image. This combined text and visual data processing supports more detailed and realistic interactions, improving AI capabilities and user experiences.

Industries Benefiting from Multimodal AI Models

Multimodal AI can dramatically enhance industries that rely heavily on different forms of data. This trend can revolutionize various sectors, including:

Healthcare

The healthcare sector is one of the prime beneficiaries of multimodal AI. With the vocabulary and vast knowledge databases of conversational AI combined with the image and pattern recognition skills of machine perception models, precise diagnoses become feasible.

Doctors use everything from typed notes to images to diagnose conditions accurately. By absorbing all these data types and coupled with advanced machine learning capabilities, multimodal AI has the potential to assist in predictive modeling and patient treatment.

Tech Companies

Technology giants like Google, Amazon, and Meta are harnessing the power of AI models, manipulating vast data sets and enhancing their services. These companies are pouring resources into the development and implementation of sophisticated systems to advance their products and services.

Services like Siri, Alexa, and Google Assistant, heavily rely on multimodal AI models. The systems analyze user interactions, both voice and text, to deliver precise responses and learn behavioral patterns for future interactions. Such applications of AI models herald a new era of digital personal assistants that are increasingly human-like in their interactions.

On another spectrum are self-learning AI systems which are being integrated into technology platforms for predictive analysis. These models analyze extensive data to recognize patterns and make accurate predictions. Courtesy of these technologies, companies can anticipate user behavior, thereby refining their services towards optimized engagement.

Transportation

The transportation sector is witnessing a massive shift with the incorporation of AI. Ride-sharing services are employing AI technologies to optimize routes, calculate accurate ETAs, and even, in the near future, autopilot cars. Many features of these models are increasingly found in self-driving vehicles, making them safer and more accessible.

Employment

AI is set to make significant shifts in the job market. We can expect to see emerging job roles, such as the AI Ethicist, AI Prompter, and AI Trainer, as the need to understand and manage AI technologies increases.

Key Components of Multimodal Models

Understanding multimodal AI models involves examining their three crucial components:

Data Integration

This step amalgamates various data sources — text, visual, and auditory, for instance — and prepares them for processing. The heterogeneous data types can contribute to building a more holistic AI model.

Algorithmic Diversity

Multimodal models utilize a blend of algorithms to interpret and analyze diverse data types. For example, you can deploy Convolutional Neural Networks (CNNs) for image processing while Natural Language Processing (NLP) algorithms interpret textual and spoken information.

Model Fusion

This is the integration phase where algorithms’ results are amalgamated for model training. Different types of fusion techniques, such as early, late, or hybrid fusion, are used depending on the application’s requirements.

These components collectively drive the functionality and performance of multimodal AI models. The merge of varied data types and algorithms enables these models to understand better and interpret context, leading to more effective decision-making and highly personalized experiences.

Example of Multimodal AI Model in Action: Google’s MUM System

The idea behind MUM is to revolutionize the way Google assists you with complicated tasks. Relying on the T5 text-to-text framework, it is 1,000 times more powerful than BERT. What makes MUM exceptional is its ability to not only understand language but also generate it. MUM training incorporates 75 different languages and different tasks simultaneously. This approach facilitates MUM’s ability to develop a thorough understanding of information and world knowledge, surpassing previous models.

The icing on the cake: Since MUM is multimodal, it can understand both text and images. This multimodal capability can even be expanded further in the future to include modalities like video and audio. This only puts us closer to the goal of resolving complicated queries with fewer searches in the future. As the vice president of Google Search, Pandu Nayak, explains:

Let’s say you are planning a hike on Mt. Fuji after an experience on Mt. Adams. You’d want to gather information about what changes you need to make in your preparation, and while Google can assist with this need, it often requires several searches — for instance, looking up each mountain’s elevation, the average temperature, the difficulty levels of the hiking trails, the most suitable gear to use, among others. Google discovered that users make an average of eight queries for tasks like these.

But, not if you rely on MUM!

MUM can understand that you’re comparing two different mountains, indicating that data regarding elevation and trail information are relevant. Additionally, MUM would understand that for a task such as mountain hiking, preparation could include fitness training and identifying necessary gear.

This could mean that one day, you simply snap a photo of your hiking boots and query, “Are these boots suitable for a Mt. Fuji hike?”, MUM’s superior understanding of images allows it to connect your picture to your question, reassuring you that your boots are up to the task. It won’t stop there — MUM might even guide you towards a blog that outlines a list of essential gear for your upcoming adventure.

Multimodal AI Models are the Future and the Future Is Now

The rise of multimodal AI models gives us a glimpse into a not-so-distant future where artificial intelligence will better understand and interpret our world by processing diverse inputs in sync, replicating human-like comprehension levels.

While multimodal AI models carry substantial potential, be aware that their adoption is not without challenges.

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