Learning Path
Question & Answer1
Understand Question2
Review Options3
Learn Explanation4
Explore TopicChoose the Best Answer
A
Transfer learning enhances model accuracy, allowing businesses to use less data while ensuring ethical AI usage.
B
Transfer learning complicates sequence learning, making it harder for businesses to adopt ethical AI practices.
C
Transfer learning is unrelated to sequence-to-sequence learning, and ethics do not apply in AI.
D
Sequence-to-sequence learning does not benefit from transfer learning and has no ethical implications.
Understanding the Answer
Let's break down why this is correct
Answer
Transfer learning lets a transformer model that has already learned language patterns from a large dataset be fine‑tuned on a smaller, task‑specific dataset, so it can quickly adapt to new sequence‑to‑sequence tasks like translation or summarization without starting from scratch. By reusing the encoder‑decoder weights, the model retains powerful language representations, which speeds up training and improves accuracy on limited data. For example, a transformer pretrained on millions of English sentences can be fine‑tuned with a few thousand bilingual pairs to produce high‑quality machine translation, saving time and computational resources. Businesses must consider fairness, avoiding bias that may be present in the pretraining data, and ensure transparency so users understand how the model works. They should also protect privacy by anonymizing data, comply with data‑protection laws, and monitor for unintended harmful outputs.
Detailed Explanation
Transfer learning lets a model that has already learned many patterns from a big data set be fine‑tuned for a new task with only a little extra data. Other options are incorrect because People think transfer learning makes sequence learning harder, but it actually does the opposite; Transfer learning is not unrelated; it is a core part of modern transformers.
Key Concepts
Transfer Learning
Sequence-to-Sequence Learning
AI Ethics in Business
Topic
Transformer Architecture
Difficulty
hard level question
Cognitive Level
understand
Practice Similar Questions
Test your understanding with related questions
1
Question 1In the context of transformer architecture, what is the main purpose of fine-tuning a pre-trained model for a specific business application?
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Question 2In the context of Transformer architecture, how does self-attention enhance the process of transfer learning?
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Question 3How does the concept of Multi-Head Attention in Transformer Architecture enhance the capabilities of Deep Learning Models in the context of Transfer Learning?
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4
Question 4How can transfer learning in transformer architecture improve sequence-to-sequence learning, and what ethical considerations should businesses keep in mind when implementing these AI technologies?
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Question 5In the context of transformer architecture, what is the main purpose of fine-tuning a pre-trained model for a specific business application?
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Question 6In the context of Transformer architecture, how does self-attention enhance the process of transfer learning?
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Question 7How does the concept of Multi-Head Attention in Transformer Architecture enhance the capabilities of Deep Learning Models in the context of Transfer Learning?
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