Intro to Neural Networks + Overview of AI Architectures AI Architecture for Different Data Modalities
Lesson Objectives
By the end of this lesson, students will be able to:
- Identify AI architectures best suited for structured, image, text, and sequential data.
- Explain the key challenges associated with different data modalities.
- Select and justify the most appropriate AI model based on data type and real-world constraints.
- Describe how multimodal AI systems integrate multiple architectures for complex tasks.
Understanding Data Modalities in AI
AI models are designed to process different types of data, referred to as data modalities. The effectiveness of an AI system depends on selecting an architecture that aligns with the characteristics and constraints of the underlying data.
Common Data Modalities and Their Challenges
graph TD;
A[Structured Data] -->|Tabular, Databases| B[Feedforward Neural Networks - FNNs];
C[Image Data] -->|Pixel-Based| D[Convolutional Neural Networks - CNNs];
E[Text Data] -->|Sequences of Words| F[Transformers];
G[Sequential Data] -->|Time-Series, Audio| H[RNNs, Transformers];
Key Challenges by Data Type:
- Structured Data: Often requires extensive feature engineering; deep learning may not always be the most efficient approach.
- Image Data: High-dimensional, requires spatial feature extraction.
- Text Data: Requires contextual understanding; traditional sequence models struggle with long-range dependencies
- Sequential Data: Capturing temporal dependencies is challenging, especially for long sequences.
Choosing the Right AI Model
Each AI architecture is optimized for specific data types:
| Data Type | Best AI Architecture | Example Use Cases |
|---|---|---|
| Structured Data | Feedforward Neural Networks (FNNs) | Credit scoring, fraud detection |
| Image Data | Convolutional Neural Networks (CNNs) | Object recognition, medical imaging |
| Text Data | Transformers (e.g., BERT, GPT) | Sentiment analysis, chatbots |
| Sequential Data | RNNs, Transformers | Speech recognition, stock price forecasting |
Key Takeaway: The success of an AI system depends not only on model complexity, but on how well the chosen architecture aligns with the data modality and problem constraints.
Exploring Multimodal AI Systems
What is Multimodal AI?
Multimodal AI systems combine multiple types of input data, such as text, images, and audio, to improve predictions and decision-making. These systems leverage different AI architectures for each modality and fuse their outputs.
Example Applications:
- Self-driving cars: Combine cameras (CNNs), lidar data (FNNs), and textual navigation inputs (Transformers).
- Medical diagnosis: Integrate patient history (FNNs) with medical imaging (CNNs) and doctor notes (Transformers).
- E-commerce recommendations: Fuse purchase history (FNNs), product images (CNNs), and customer reviews (Transformers).
Coding Walkthrough: Implementing a Multimodal AI System
Let’s implement a basic multimodal model that:
- Uses a CNN to process image data.
- Uses a Transformer-based model to process text.
- Combines both representations to make a final classification decision.
Python Code:
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from transformers import TFAutoModel
# Define image processing branch (CNN)
image_input = keras.Input(shape=(64, 64, 3))
x = layers.Conv2D(32, (3,3), activation='relu')(image_input)
x = layers.MaxPooling2D((2,2))(x)
x = layers.Flatten()(x)
image_features = layers.Dense(128, activation='relu')(x)
# Define text processing branch (Transformer)
text_input = keras.Input(shape=(128,)) # Assume we have tokenized text
text_model = TFAutoModel.from_pretrained("distilbert-base-uncased")
text_features = text_model(text_input)[0][:, 0, :]
text_features = layers.Dense(128, activation='relu')(text_features)
# Combine both modalities
combined = layers.concatenate([image_features, text_features])
output = layers.Dense(1, activation='sigmoid')(combined)
# Create and compile model
multimodal_model = keras.Model(inputs=[image_input, text_input], outputs=output)
multimodal_model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
# Summary of the multimodal model
multimodal_model.summary()
Discussion Questions:
- How does combining multiple data types improve model performance?
- What are the biggest challenges in training multimodal AI systems?
- Can you think of other applications where multimodal AI would be useful?
Summary & Key Takeaways
- Different AI architectures are optimized for specific data modalities..
- Multimodal AI integrates multiple architectures to process heterogeneous data sources.
- Combining text, images, and structured data enables more accurate predictions.
- Training multimodal models is more complex but allows AI systems to make better-informed decisions.