Multimodal AI is a type of artificial intelligence (AI) that can process, understand and/or generate outputs for more than one type of data. Modality refers to the way in which something exists, is experienced, or is expressed. In the context of machine learning (ML) and artificial intelligence, modality specifically refers to a data type. Examples of data modalities include:
· Text.
· Images.
· Audio.
· Video.
· Unimodal vs. Multimodal.
Most AI systems today are unimodal. They are designed and built to work with one type of data exclusively, and they use algorithms tailored for that modality. A unimodal AI system like ChatGPT, for example, uses natural language processing (NLP) algorithms to understand and extract meaning from text content, and the only type of output the chatbot can produce is text. In contrast, multimodal architectures that can integrate and process multiple modalities simultaneously have the potential to produce more than one type of output. If future iterations of ChatGPT are multimodal, for example, a marketer who uses the generative AI bot to create text-based web content could prompt the bot to create images that accompany the text it generates.
Working of Multimodal AI
Multimodal AI systems are structured around three basic elements: an input module, a fusion module, and an output module. The input module is a set of neural networks that can take in and process more than one data type. Because each type of data is handled by its own separate neural network, every multimodal AI input module consists of numerous unimodal neural networks. The fusion module is responsible for integrating and processing pertinent data from each data type and taking advantage of the strengths of each data type. The output module generates outputs that contribute to the overall understanding of the data. It is responsible for creating the output from the multimodal AI.
Challenges
Multimodal AI is more challenging to create than unimodal AI due to several factors. They include:
Data integration: Combining and synchronizing different types of data can be challenging because the data from multiple sources will not have the same formats. Ensuring the seamless integration of multiple modalities and maintaining consistent data quality and temporal alignment throughout the processing pipeline can be difficult and time-consuming.
Feature representation: Each modality has its own unique characteristics and representation methods. For example, images require feature extraction techniques like convolutional neural networks (CNNs), while text may require word embeddings or large language models (LLMs). It is challenging to combine and represent different modalities in a meaningful way that captures their interdependencies and enhances the overall understanding of the data.
Dimensionality and scalability: Multimodal data is typically high-dimensional, and there are no mechanisms for dimensionality reduction because each modality contributes its own set of features. As the number of modalities increases, the dimensionality of the data also grows significantly. This presents challenges in terms of computational complexity, memory requirements, and scalability for both the AI models and the algorithms they use to process data.
Model architecture and fusion techniques: Designing effective architectures and fusion techniques to combine information from multiple modalities is still an area of ongoing research. Finding the right balance between modality-specific processing and cross-modal interactions is a complex task that requires careful design and lots of experimentation.
Availability of labeled data: Multimodal AI data sets often require labeled data that covers multiple modalities. The challenges of collecting and annotating data sets with diverse modalities are difficult, and it can be expensive to maintain large-scale multimodal training datasets.
Despite these challenges, multimodal AI systems have the potential to be more user-friendly than unimodal systems and provide consumers with a more nuanced understanding of complex real-world data. Ongoing research and advancements in areas like multimodal representation, fusion techniques, and large-scale multimodal dataset management are helping to address these challenges and push the boundaries of today’s unimodal AI capabilities.
Future of Multimodal AI
In the future, as foundation models with large-scale multimodal data sets become more cost-effective, experts expect to see more innovative applications and services that leverage the power of multimodal data processing. Use cases include:
Autonomous vehicles: Autonomous vehicles will be able to process data from various sensors such as cameras, radar, GPS, and LiDAR (Light Detection and Ranging) more efficiently and make better decisions in real-time.
Healthcare: Analyzing patient data by combining medical images from X-rays or MRIs with clinical notes, and integrating sensor data from wearable devices like smart watches will improve diagnostics and provide patients with more personalized healthcare.
Video understanding: Multimodal AI can be used to combine visual information with audio, text, and other modalities to improve video captioning, video summarization, and video search.
Human-computer interaction: Multimodal AI will be employed in human-computer interaction scenarios to enable more natural and intuitive communication. This includes applications such as voice assistants that can understand and respond to spoken commands while simultaneously processing visual cues from the environment.
Content recommendation: Multimodal AI that can combine data about user preferences and browsing history with text, image, and audio data will be able to provide more accurate and relevant recommendations for movies, music, news articles, and other media.
Social media analysis: Multimodal AI that can integrate social media data, including text, images, and videos, with sentiment analysis will improve topic extraction, content moderation, and detecting and understanding trends in social media platforms.
Robotics: Multimodal AI will play a crucial role in robotics applications by allowing physical robots to perceive and interact with their environment using multiple modalities to enable more natural and robust human-robot interaction.
Smart assistive technologies: Speech-to-text systems that can combine audio data with text and image data will improve the user experience (UX) for visually impaired individuals and gesture-based control systems.
