Multimodal AI (text, image, video, audio) in AI for Everyone - Mini Project: Build & Apply

Understanding Multimodal AI (text, image, video, audio)

📖 Scenario: You are learning about how modern AI systems can understand and work with different types of information like text, images, videos, and sounds. This helps AI do many useful things like recognizing objects in photos, understanding spoken words, or describing videos.

🎯 Goal: Build a simple structured overview that lists examples of AI tasks for each type of data: text, image, video, and audio. This will help you remember how AI uses different kinds of information.

📋 What You'll Learn

Create a dictionary called multimodal_ai_tasks with keys for 'text', 'image', 'video', and 'audio'

Add a variable called example_count and set it to 2

Use a dictionary comprehension to create a new dictionary called limited_tasks that keeps only the first example_count tasks for each data type

Add a final key-value pair to limited_tasks with key 'summary' and value 'This dictionary shows AI tasks by data type with limited examples.'

💡 Why This Matters

🌍 Real World

Multimodal AI is used in apps like voice assistants, photo tagging, and video analysis to understand different types of information together.

💼 Career

Understanding multimodal AI helps in roles like AI development, data science, and product design where combining text, images, video, and audio is common.

1

Create the initial data structure

Create a dictionary called multimodal_ai_tasks with these exact entries: 'text' mapped to a list with 'Language translation' and 'Sentiment analysis', 'image' mapped to a list with 'Object detection' and 'Image captioning', 'video' mapped to a list with 'Action recognition' and 'Video summarization', and 'audio' mapped to a list with 'Speech recognition' and 'Sound classification'.

AI for Everyone

# Create the multimodal_ai_tasks dictionary with the specified keys and lists
# Your code here

Need a hint?

Use a dictionary with keys 'text', 'image', 'video', and 'audio'. Each key should have a list of two example AI tasks as values.

2

Add a configuration variable

Add a variable called example_count and set it to the number 2.

AI for Everyone

multimodal_ai_tasks = {
    'text': ['Language translation', 'Sentiment analysis'],
    'image': ['Object detection', 'Image captioning'],
    'video': ['Action recognition', 'Video summarization'],
    'audio': ['Speech recognition', 'Sound classification']
}
# Define example_count variable
# Your code here

Need a hint?

Just create a variable named example_count and assign it the value 2.

3

Apply the main concept with dictionary comprehension

Use a dictionary comprehension to create a new dictionary called limited_tasks that contains the same keys as multimodal_ai_tasks, but each value list is sliced to keep only the first example_count items.

AI for Everyone

multimodal_ai_tasks = {
    'text': ['Language translation', 'Sentiment analysis'],
    'image': ['Object detection', 'Image captioning'],
    'video': ['Action recognition', 'Video summarization'],
    'audio': ['Speech recognition', 'Sound classification']
}
example_count = 2
# Create limited_tasks dictionary using dictionary comprehension
# Your code here

Need a hint?

Use {key: value[:example_count] for key, value in multimodal_ai_tasks.items()} to create the new dictionary.

4

Add the final summary entry

Add a new key-value pair to the limited_tasks dictionary with key 'summary' and value 'This dictionary shows AI tasks by data type with limited examples.'.

AI for Everyone

multimodal_ai_tasks = {
    'text': ['Language translation', 'Sentiment analysis'],
    'image': ['Object detection', 'Image captioning'],
    'video': ['Action recognition', 'Video summarization'],
    'audio': ['Speech recognition', 'Sound classification']
}
example_count = 2
limited_tasks = {key: value[:example_count] for key, value in multimodal_ai_tasks.items()}
# Add the summary key-value pair to limited_tasks
# Your code here

Need a hint?

Use limited_tasks['summary'] = 'This dictionary shows AI tasks by data type with limited examples.' to add the summary.