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What Does Input Module Do in Multimodal AI Systems Exactly?
Understand the input module’s role in multimodal AI—specialized networks per data type like text/images/audio—vs. fusion/output, with architecture breakdowns for certification prep on processing diverse inputs.
Question
Which of the following describes the role of the input module in a multimodal AI system?
A. It takes in all data types and transforms them into a common format.
B. It generates the final output for the user.
C. It contains specialized networks for handling each different type of data.
D. It is only used for text-based prompts.
Answer
C. It contains specialized networks for handling each different type of data.
Explanation
In multimodal AI systems, the input module acts as the initial processing layer composed of specialized unimodal neural networks—such as vision transformers for images, BERT-like encoders for text, or spectrogram CNNs for audio—that independently extract features from their respective data types (e.g., token embeddings from text, patch embeddings from images), preprocessing raw inputs into compatible vector representations before fusion, ensuring modality-specific handling preserves unique structural information like spatial hierarchies in visuals or sequential dependencies in language.
Option A describes fusion module responsibilities, where transformed embeddings align into a shared space. Option B pertains to the output module generating user-facing results. Option D restricts to unimodal text systems, ignoring multimodal diversity.