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. Or, 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.

OpenAI launched ChatGPT in November 2022, which quickly put generative AI on the map. ChatGPT was unimodal AI, designed to receive text inputs, and generate text outputs by using natural language processing (NLP). Multimodal AI makes gen AI more robust and useful by allowing multiple types of inputs and outputs. Dall-e, for example, was Open AI’s initial multimodal implementation of its GPT model, but GPT-4o introduced multimodal capabilities to ChatGPT as well.

Multimodal AI models can combine information from various data sources and across media to provide a more comprehensive and nuanced understanding of the data. This allows the AI to make better-informed decisions and generate more accurate outputs. By leveraging different modalities, multimodal AI systems can achieve higher accuracy and robustness in tasks such as image recognition, language translation and speech recognition. The integration of different types of data helps in capturing more context and reducing ambiguities. Multimodal AI systems are more resilient to noise and missing data. If one modality is unreliable or unavailable, the system can rely on other modalities to maintain performance.

Multimodal AI enhances human-computer interaction by enabling more natural and intuitive interfaces for better user experiences. For instance, virtual assistants can understand and respond to both voice commands and visual cues, making interactions smoother and more efficient. Imagine a chatbot that can talk to you about your glasses and make sizing recommendations based on a photo that you share with it, or a bird identification app that can recognize images of a particular bird, and confirm its identification by “listening” to an audio clip of its song. AI that can operate across multiple sensory dimensions can give users more meaningful outputs, and more ways to engage with data