What is Multimodal AI?

What is Multimodal AI?

In November 2022, OpenAI launched ChatGPT, which quickly took the world by storm with its unprecedented capabilities. This marked the beginning of the generative AI revolution, leading everyone to wonder: what’s next?

At the time, ChatGPT and many other generative AI tools powered by Large Language Models (LLMs) were designed to process text inputs and generate text outputs, making them unimodal AI tools. However, this was just the beginning. The progress in the AI industry has been astonishing, pushing the boundaries of what’s possible. Today, the answer to "What’s next?" is multimodal learning, a trend set to redefine AI's capabilities.

Multimodal generative AI models combine various types of inputs and can create outputs that include multiple types of data. In this guide, we will explore the concept of multimodal AI, its core principles, underlying technologies, applications, and implementation challenges.

Understanding Multimodal AI

While most advanced generative AI tools are not yet capable of thinking like humans, they deliver groundbreaking results that bring us closer to the threshold of Artificial General Intelligence (AGI). AGI refers to a hypothetical AI system that can understand, learn, and apply knowledge across a wide range of tasks, much like a human.

A central question in achieving AGI is understanding how humans learn. Our brains rely on our five senses to collect information, store it in memory, process it to gain insights and leverage it to make decisions. Early generative AI models, like ChatGPT, were unimodal, processing only one type of data (usually text) to generate similar outputs.

Multimodal learning augments the learning capacity of machines by training them with large amounts of text and other sensory data, such as images, videos, or audio. This allows models to learn patterns and correlations between different types of data, unlocking new possibilities for intelligent systems. For instance, GPT-4, the foundation model of ChatGPT, can accept both image and text inputs and generate text outputs, while OpenAI's Sora model can handle text-to-video tasks.

Core Concepts of Multimodal AI

Multimodal AI adds complexity to state-of-the-art LLMs. These models are based on a neural architecture called a Transformer, which uses an encoder-decoder architecture and an attention mechanism for efficient data processing.

To integrate different data types, multimodal AI relies on data fusion techniques, classified into three categories:

• Early Fusion: Encodes different modalities into a common representation space early in the processing pipeline, resulting in a single output that captures semantic information from all modalities.

• Mid Fusion: Combines modalities at different preprocessing stages through special layers in the neural network designed for data fusion.

• Late Fusion: Processes each modality separately through different models and then combines the outputs at a later stage.

Technologies Powering Multimodal AI

Multimodal AI is built on knowledge from multiple AI subfields. Here are some domains fueling the multimodal AI boom:

• Deep Learning: Employs algorithms like neural networks to address complex tasks. Advances in transformers are critical for the future of multimodal AI.

• Natural Language Processing (NLP): Bridges the gap between human communication and computer understanding, essential for high-performance generative AI models.

• Computer Vision: Techniques that enable computers to "see" and understand images, crucial for processing visual data in multimodal models.

• Audio Processing: Allows AI to interpret and generate audio, expanding the scope of multimodal applications from voice recognition to music creation.

Applications of Multimodal AI

• Healthcare: Multimodal AI is transforming healthcare by integrating data from electronic health records, medical imaging, and genomic sequencing. This holistic approach enables accurate diagnosis, personalized treatment plans, and improved patient outcomes. For example, AI systems can analyze radiology images alongside patient histories to detect anomalies more accurately and suggest tailored treatments.

• Education: In education, multimodal AI enhances learning experiences by combining text, images, and audio-visual content to create more engaging and interactive educational tools. AI-driven platforms can offer personalized learning paths by analyzing student performance data from various modalities, such as written assignments, spoken answers, and video submissions. This helps cater to diverse learning styles and needs.

• Banking and Finance: The banking and finance sectors benefit from multimodal AI by using it to detect fraud, assess credit risks, and improve customer service. AI systems can analyze transaction data, voice recordings from customer service interactions, and social media activity to identify fraudulent patterns and make real-time decisions. Additionally, AI can provide financial advice by synthesizing market trends, customer financial history, and economic indicators.

• Government: Governments are leveraging multimodal AI to enhance public services, improve security, and streamline administrative processes. For instance, AI can analyze surveillance footage, social media posts, and public records to detect and prevent criminal activities. In public administration, AI can automate the processing of various documents, such as tax forms and applications, by understanding and integrating data from text, images, and other formats.

• Law: In the legal field, multimodal AI assists in document analysis, case research, and evidence examination. AI systems can process large volumes of legal documents, extracting relevant information and cross-referencing it with case laws and precedents. This speeds up legal research and helps lawyers build stronger cases. Additionally, AI can analyze video and audio evidence, providing insights that might be missed by human reviewers.

Challenges and Risks

Implementing multimodal AI poses challenges, such as finding suitable use cases, addressing the data literacy skill gap, and managing the high costs of computing resources. These factors can make it difficult for organizations to deploy multimodal AI effectively.

Several potential pitfalls accompany multimodal AI:

• Lack of Transparency: Multimodal models are often "black boxes," making it hard to understand their reasoning.

• Monopoly Concerns: The development and operation of multimodal AI are dominated by a few Big Tech companies, although open-source models are emerging.

• Bias and Discrimination: Multimodal AI can inherit biases from training data, leading to unfair decisions.

• Privacy Issues: Handling vast amounts of data, including personal information, raises privacy and security concerns.

• Environmental Impact: Training and operating multimodal models consume significant resources, contributing to environmental footprints.

The Future of Multimodal AI

Multimodal AI is the next frontier in the AI revolution, with ongoing research and development expanding its applications. However, it also brings significant risks and challenges that need addressing to ensure a fair and sustainable future. As new techniques emerge, the potential for multimodal AI will continue to grow, driving innovation across various industries​

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