Multi-modal inputs refer to data streams that originate from different types of sources or modalities, such as text, images, audio, or structured numerical data. Systems designed to process multi-modal inputs can synthesize information from these varied formats to gain a more comprehensive understanding of a subject. This integration allows for richer representations and more robust analytical capabilities. It enables machines to interpret complex real-world phenomena more accurately.
Context
In the digital asset landscape, multi-modal inputs are increasingly used for advanced market analysis, risk assessment, and fraud detection. For example, combining crypto news sentiment (text) with trading volume (numerical) and social media trends (text/images) can yield deeper insights. News reports may highlight AI systems that leverage these diverse data types to predict market movements or identify coordinated activities. This approach provides a holistic view of market dynamics.
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