Multimodal learning / multimodal AI
• Multimodal AI - or multimodal learning - mimics the human brain’s ability to simultaneously process textual, visual, and audio information, enabling a more nuanced understanding of reality.
• Transitioning from a unimodal model (like those specialized in text, images, or sounds) to a multimodal model presents technical challenges, particularly in creating shared representations for different types of data.
• Multimodal AI offers advantages such as capturing more comprehensive knowledge of the environment and enabling new applications, like merging data from various modalities for complex tasks.
Watch the video
• Transitioning from a unimodal model (like those specialized in text, images, or sounds) to a multimodal model presents technical challenges, particularly in creating shared representations for different types of data.
• Multimodal AI offers advantages such as capturing more comprehensive knowledge of the environment and enabling new applications, like merging data from various modalities for complex tasks.


When will we see living robots? The challenges facing biohybrid robotics
Read the article

A mathematical model to help AIs anticipate human emotions
Read the article
David Caswell: “All journalists should be trained to use generative AI”
Read the article
Health: Jaide aims to reduce diagnostic errors with generative AI
Read the article