MULTIMODAL AI

Multimodal AI refers to machine learning models capable of processing and integrating information from multiple modalities or types of data. These modalities can include text, images, audio, video and other forms of sensory input.

Unlike traditional AI models that are typically designed to handle a single type of data, multimodal AI combines and analyzes different forms of data inputs to achieve a more comprehensive understanding and generate more robust outputs. As an example, a multimodal model can receive a photo of a landscape as an input and generate a written summary of that place’s characteristics. It could receive a written summary of a landscape and generate an image based on that description. This ability to work across multiple modalities gives these models powerful capabilities.

How multimodal AI works

Artificial intelligence is a rapidly evolving field in which the latest advances in training algorithms to build foundation models are being applied to multimodal research. This discipline saw prior multimodal innovations such as audio-visual speech recognition and multimedia content indexing, which had developed before advances in deep learning and data science paved the way for gen AI.

Multimodal models add a layer of complexity to large language models?(LLMs), which are based on transformers, themselves built on an encoder-decoder architecture with an attention mechanism to efficiently process data. Multimodal AI uses data fusion techniques to integrate different modalities. This fusion can be described as early (when modalities are encoded into the model to create a common representation space) mid (when modalities are combined at different preprocessing stages) and late (when multiple models process different modalities and combine the outputs).

Trends in multimodal AI

Multimodal AI is a rapidly evolving field, with several key trends shaping its development and application. Here are some of the notable trends:

Unified models

OpenAI’s GPT-4 V(ision, Google’s Gemini, and other unified models are designed to handle text, images and other data types within a single architecture. These models can understand and generate multimodal content seamlessly.

Enhanced cross-modal interaction

Advanced attention mechanisms and transformers are being used to better align and fuse data from different formats, leading to more coherent and contextually accurate outputs.

Real-time multimodal processing

Applications in autonomous driving and augmented reality, for example, require AI to process and integrate data from various sensors (cameras, LIDAR and more.) in real-time to make instantaneous decisions.

Multimodal data augmentation

Researchers are generating synthetic data that combines various modalities (for example., text descriptions with corresponding images) to augment training datasets and improve model performance.

Open source and collaboration

Initiatives like Hugging Face and Google AI are providing open-source AI tools, fostering a collaborative environment for researchers and developers to advance the field.

Multimodal AI use cases

Multimodal AI is an exciting development, but it has a long way to go. Even still, the possibilities are nearly endless. A few ways we can use multimodal artificial intelligence include:

• Improving the performance of self-driving cars?by combining data from multiple sensors (e.g. cameras, radar, and lidar).
• Developing new medical diagnostic tools?that use data such as images from scans, health records, and genetic testing results.
• Improving chatbot and virtual assistant experiences?by processing a variety of inputs and creating more sophisticated outputs.
• Employing improved fraud detection and risk assessment in banking, finance, and other industries.? ?
• Analyzing social media data? including text, images, and videos for improved content moderation and trend detection.
• Allowing robots to better understand and interact with their environment, leading to more human-like behavior and abilities.

The Challenges of Implementing Multimodal AI Solutions

The multimodal AI boom comes with endless possibilities for businesses, governments and individuals. However, as with any nascent technology, implementing them in your daily operations can be challenging.

Firstly, you need to find the use cases that match your specific needs. Moving from concept to deployment is not always easy, especially if you lack the people who properly understand the technicalities behind multimodal AI. However, given the current data literacy skill gap, finding the right people to put your models in production may be hard and costly, for companies are willing to pay high numbers to attract such a limited talent.

Finally, when speaking about generative AI, mentioning affordability is mandatory. These models, especially multimodal ones, require considerable computing resources to work, and that means money. Hence, before adopting any generative AI solution, it’s important to estimate the resources you want to invest.

The Future of Multimodal AI

Multimodal AI is certainly the next frontier of the generative AI revolution. The rapid development of the field of multimodal learning is fueling the creation of new models and applications for all kinds of purposes. We are just at the beginning of this revolution. As new techniques are developed to combine more and new modalities, the scope of multimodal AI will widen. However, with great power comes great responsibility. Multimodal AI comes with serious risks and challenges that need to be addressed to ensure a fair and sustainable future.