What does LLM stand for in the context of AI?

• Large Learning Machine

• Large Language Model

• Limited Language Model

• Linguistic Logic Model

Name one popular application of generative AI.

• DALL-E

• Bard

• Midjourney

• ChatGPT

How does generative AI differ from traditional AI?

• Generative AI only processes data, while traditional AI generates new content.

• Generative AI creates new content, while traditional AI analyzes existing data.

• Generative AI is limited to text, whereas traditional AI can handle images and videos.

• Generative AI requires more data than traditional AI to function effectively.

What is the primary function of a language model?

• To store large amounts of data efficiently.

• The primary function of a language model is to generate and understand human language.

• To analyze the sentiment of a text.

• To translate text between languages.

Can you give an example of a generative AI tool?

• Adobe Photoshop

• Google Docs

• OpenAI's ChatGPT

• Microsoft Word

What are some ethical concerns associated with generative AI?

• Ethical concerns include misinformation, copyright issues, bias, and job displacement.

• Enhanced data privacy and security

• Improved decision-making in healthcare

• Increased creativity in art and music

How do LLMs learn from data?

• LLMs only learn from structured data like spreadsheets.

• LLMs learn from data by training on large datasets using neural networks to identify patterns and optimize predictions.

• LLMs learn by memorizing all the data without any processing.

• LLMs do not require any data to learn and can generate text randomly.

What role does training data play in the performance of LLMs?

• Training data significantly impacts the performance of LLMs by shaping their knowledge and response capabilities.

• LLMs perform better without any training data.

• Training data has no effect on LLMs' performance.

• The amount of training data is irrelevant to LLMs' capabilities.

What is the significance of fine-tuning in LLMs?

• Fine-tuning is only necessary for training LLMs from scratch.

• Fine-tuning allows LLMs to adapt to specific tasks or domains, improving their performance and relevance.

• Fine-tuning eliminates the need for any pre-training in LLMs.

• Fine-tuning reduces the size of LLMs, making them faster.

How can generative AI impact creative industries?

• Generative AI is only useful for technical industries, not creative ones.

• Generative AI has no impact on productivity in creative fields.

• Generative AI can replace all human creativity entirely.

• Generative AI can revolutionize creative industries by enhancing productivity, enabling new forms of expression, and automating routine tasks.