Example Use Cases

Explore real-world applications of XAI techniques across different domains. These examples demonstrate how our tools can be applied to understand and explain complex AI systems.

Large Language Model (ChatGPT) Embeddings Analysis

Visualize and understand how language models represent text in high-dimensional space. Our tools help analyze semantic relationships, attention patterns, and decision boundaries in transformer-based models.

Embeddings Attention Semantics NLP 
from trinity import analyze_embeddings

# Analyze token embeddings
embeddings = model.get_embeddings(text)
attention_patterns = analyze_embeddings(
    embeddings,
    method='attention_flow'
)
LLM Analysis

Brain-Computer Interface Decoders

Interpret neural decoders by visualizing brain activity patterns and understanding how they map to intended actions. Essential for developing reliable and transparent BCI systems.

Neural BCI Decoding Neuroscience 
from trinity import decode_neural

# Analyze neural patterns
decoded_signals = decode_neural(
    brain_activity,
    temporal_window=500,
    spatial_filter='csp'
)
BCI Analysis

Deep Learning Object Detection Models

Understand how object detection models process visual information and make decisions. Visualize feature maps, attention regions, and decision processes in convolutional neural networks.

CNN Vision Detection Features 
from trinity import analyze_detection

# Analyze detection process
feature_maps = analyze_detection(
    model,
    image,
    layer='conv5',
    attention=True
)
Object Detection Analysis