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AIKA vs. PyTorch: A Comparison

AIKA takes a fundamentally different approach compared to traditional deep learning frameworks like PyTorch.

Key Differences

Feature	AIKA	PyTorch
Computation Model	Event-driven processing via an activation queue	Batch-based matrix operations
Network Structure	Dynamically instantiated object graph	Predefined tensor dimensions
Processing	Asynchronous, sparse activations	Synchronous, dense computations
Architecture	Type-based hierarchy defining neural elements	Layered neural networks using tensor algebra
Flexibility	Neurons and connections are instantiated at runtime	Fixed-size tensors and layers

Advantages of AIKA

• Sparse & Efficient: AIKA only activates relevant parts of the network, reducing computation time.
• More Interpretable: Individual activations can be traced back to symbolic representations.
• Flexible Topology: The network structure evolves dynamically rather than being fixed.

To get started with AIKA, visit the Installation Guide.