We're emulating the brain —
in silicon

We are a group of engineers, theorists, and scientists seeking to reverse-engineer principles of neural design and apply them to solve societal problems. Our philosophy is forward-looking. We anticipate problems hundreds to thousands of times more complex than those of today. Thus we seek technological solutions that handle this complexity efficiently and scalably.

Today's deep learning solutions scale poorly. Emitting 10,000-fold more carbon to train a deep net only doubles its performance on benchmark tasks. The next doubling in performance will emit as much CO₂ as New York City does in a month.

In contrast, the brain learns extremely efficiently. A child masters language by the age of 6, having heard at most 65 million words. That’s 15,000-fold less than the trillion words used to train GPT-3. Equivalently, a child that learns language at the same rate as GPT-3 would be 90,000 years old before it could converse fluently. By reverse-engineering how the brain uses so little data to learn, we hope to invent solutions that enable a sustainable technological future.