Efficiency
Sparse intelligence
Biological brains compute with extreme sparsity — only a small fraction of neurons fire at any moment. Sparse intelligence asks how much of that efficiency can transfer to engineered AI.
Key takeaways
- Brains run on roughly 20W; comparable AI workloads use orders of magnitude more.
- Sparse activations enable lower energy per computation in both biology and silicon.
- Mixture-of-experts and conditional computation are mainstream AI's first big bets on sparsity.
Why sparsity is interesting
Sparse codes are energy-efficient, robust to noise, and conducive to fast, content-addressable retrieval. All three are weaknesses of dense, always-on neural networks at scale.
Where it appears in modern AI
Mixture-of-experts routing, conditional computation, structured pruning, and event-driven neuromorphic hardware all rely on sparsity as a design primitive.