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The Future of AI is Multimodal
Originally published on Substack.
From AI’s early days of simple, rule-based algorithms, it has journeyed through remarkable advancements, each milestone bringing it a step closer to mirroring the intricacies of human intelligence. A significant leap in this journey is the emergence of multimodal AI — a paradigm shift from the traditional unimodal systems that once dominated the field.
Taking a step back, let’s start with a definition of “multimodal AI”: Multi-modal AI refers to artificial intelligence systems that can process and interpret data from multiple different modes or types of input, such as text, images, audio, and video. These systems can analyze and understand the information from these diverse sources simultaneously, enabling more comprehensive and nuanced decision-making and interactions. For example, a multi-modal AI could analyze a video by understanding the spoken words (audio), recognizing objects and actions (visual), and interpreting the text in subtitles or captions (textual), all at the same time.
Unlike its predecessors, which relied on single data input types like text or images, multimodal AI represents a more holistic approach to machine learning. By integrating multiple types of data inputs — text, images, and sounds — it offers a richer, more nuanced understanding of the world. This integration mirrors the human…
