Question:
The passage implies that the convergence of deep learning techniques across computer science fields has had what effect?
The passage implies that the convergence of deep learning techniques across computer science fields has had what effect?
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Think about the literal meaning of words in the question. "Convergence" means coming together. "Multimodal" means having multiple modes (e.g., vision, language). It is logical to infer that the coming together of the underlying technology would enable the coming together of the applications.
Updated On: Sep 30, 2025
- It has resulted in a significant increase in the size and complexity of language-only models.
- It has made it easier to integrate models from different domains into multimodal systems.
- It has caused a decline in the importance of natural language processing in AI research.
- It has rendered the use of computer vision and robotics obsolete in deep learning applications.
- It has led to a reduction in the overall computational power required for training AI models.
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The Correct Option is B
Solution and Explanation
Step 1: Understanding the Concept:
This is an implication question asking about the effect of "convergence of deep learning techniques." The passage's main topic is multimodality, which is the integration of different AI domains (language, vision, etc.). The question connects deep learning to this central theme.
Step 2: Detailed Explanation:
"Convergence" means things are coming together. If deep learning techniques are converging across fields like natural language processing and computer vision, it means these fields are starting to use similar underlying methods and architectures. When different fields use a common technological foundation, it becomes much easier to combine them. Therefore, the convergence of deep learning techniques would logically be the enabler of multimodal systems, which are, by definition, an integration of models from different domains.
(A) This focuses only on language-only models, while the passage seems to be about the shift \textit{towards} multimodality.
(B) This is the correct answer. The convergence of underlying techniques (deep learning) is what makes the integration of different applications (language, vision) into a single multimodal system possible or easier.
(C) & (D): Convergence would make these fields \textit{more} important as components of larger systems, not less important or obsolete.
(E) This is highly unlikely. Deep learning and the integration of large models are known to be computationally intensive, so a reduction in required power would not be a logical effect.
Step 3: Final Answer:
The convergence of deep learning provides a common technical language for different AI fields, which has the direct effect of making it easier to integrate them into complex multimodal systems.
This is an implication question asking about the effect of "convergence of deep learning techniques." The passage's main topic is multimodality, which is the integration of different AI domains (language, vision, etc.). The question connects deep learning to this central theme.
Step 2: Detailed Explanation:
"Convergence" means things are coming together. If deep learning techniques are converging across fields like natural language processing and computer vision, it means these fields are starting to use similar underlying methods and architectures. When different fields use a common technological foundation, it becomes much easier to combine them. Therefore, the convergence of deep learning techniques would logically be the enabler of multimodal systems, which are, by definition, an integration of models from different domains.
(A) This focuses only on language-only models, while the passage seems to be about the shift \textit{towards} multimodality.
(B) This is the correct answer. The convergence of underlying techniques (deep learning) is what makes the integration of different applications (language, vision) into a single multimodal system possible or easier.
(C) & (D): Convergence would make these fields \textit{more} important as components of larger systems, not less important or obsolete.
(E) This is highly unlikely. Deep learning and the integration of large models are known to be computationally intensive, so a reduction in required power would not be a logical effect.
Step 3: Final Answer:
The convergence of deep learning provides a common technical language for different AI fields, which has the direct effect of making it easier to integrate them into complex multimodal systems.
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