Generative AI is distinct from other types of AI in that it focuses on creating new content by learning patterns from existing data. This includes generating text, images, audio, and other types of media. Unlike AI that primarily analyzes data to make decisions or predictions, Generative AI actively creates new and original outputs. This ability to generate diverse content is a hallmark of Generative AI models like GPT-4, which can produce human-like text, create images, and even compose music based on the patterns they have learned from their training data.
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Question 2
What is the purpose of Attention Mechanism in Transformer architecture?
Options:
A.
Weigh the importance of different words within a sequence and understand the context.
B.
Convert tokens into numerical forms (vectors) that the model can understand.
C.
Break down a sentence into smaller pieces called tokens.
D.
Apply a specific function to each word individually.
Answer:
A
Explanation:
The purpose of the Attention Mechanism in Transformer architecture is to weigh the importance of different words within a sequence and understand the context. In essence, the attention mechanism allows the model to focus on specific parts of the input sequence when producing an output, which is crucial for understanding context and maintaining coherence over long sequences. It does this by assigning different weights to different words in the sequence, enabling the model to capture relationships between words that are far apart and to emphasize relevant parts of the input when generating predictions.
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Question 3
What key objective does machine learning strive to achieve?
Options:
A.
Enabling computers to learn and improve from experience
B.
Creating algorithms to solve complex problems
C.
Improving computer hardware
D.
Explicitly programming computers
Answer:
A
Explanation:
The key objective of machine learning is to enable computers to learn from experience and improve their performance on specific tasks over time. This is achieved through the development of algorithms that can learn patterns from data and make decisions or predictions without being explicitly programmed for each task. As the model processes more data, it becomes better at understanding the underlying patterns and relationships, leading to more accurate and efficient outcomes.