The AI technology described is a multi-modal model because it is designed to process and integrate information from multiple distinct data types, or "modalities." In this case, the modalities are text, video, images, and sound. Multi-modal AI systems leverage the combination of these different data sources to achieve a more comprehensive and contextually rich understanding of the input, which is essential for tasks like accurately tagging diverse content. This approach mimics the human ability to synthesize information from various senses to interpret the world.

A. Deductive inference: This is a form of logical reasoning that derives specific conclusions from general premises, not a classification of an AI model based on its input data types.

C. Transformative AI: This is a broad, strategic term describing AI with a significant societal or business impact, not a specific technical category for a model's architecture.

D. Expert system: This is a type of AI that emulates the decision-making of a human expert in a narrow domain, typically using a predefined knowledge base and rules, rather than processing multiple raw data types.

1. Baltrušaitis

T.

Ahuja

C.

& Morency

L. P. (2018). "Multimodal Machine Learning: A Survey and Taxonomy." IEEE Transactions on Pattern Analysis and Machine Intelligence

41(2)

423-443. Section I (Introduction) defines multimodality as building models that can "process and relate information from multiple modalities

" listing examples such as video

audio

and text. (DOI: https://doi.org/10.1109/TPAMI.2018.2798607)

2. Stanford University. (2021). CS224N: NLP with Deep Learning

Winter 2021

Lecture 15: Multimodality and Language. Slide 4

"What is multimodality?"

defines the concept as connecting language to other modalities like vision (images

video) and audio

which directly aligns with the scenario described in the question. (Available at: http://web.stanford.edu/class/cs224n/slides/cs224n-2021-lecture-15-multimodality.pdf)

3. Russell

S. J.

& Norvig

P. (2020). Artificial Intelligence: A Modern Approach (4th ed.). Pearson. Chapter 8

"First-Order Logic

" discusses inference methods like deduction. Chapter 9

"Inference in First-Order Logic

" details the mechanisms. This source distinguishes logical reasoning from model architectures.