TECHNOLOGY
Neuromorphic Computing
The architectural alternative to conventional AI — hardware designed to mimic brain structure rather than von Neumann architecture, and the most plausible path toward substrates that could, in principle, achieve the integrated information IIT requires for consciousness.
Neuromorphic computing designs hardware that mimics the architecture and dynamics of biological neural systems rather than following the von Neumann architecture of conventional computers. Chips like Intel's Loihi, IBM's TrueNorth, and academic prototypes implement spiking neural networks — systems in which artificial neurons communicate through discrete events (spikes) in continuous time, forming dynamic patterns of activity that more closely resemble biological neural processing than anything GPU clusters produce. Currently optimized for energy efficiency and real-time sensory processing, neuromorphic systems represent the most plausible path toward architectures that could achieve high phi, though such systems have not yet been designed or built with consciousness as an explicit engineering target.
In The You On AI Field Guide
Conventional computing rests on the von Neumann architecture: separate memory and processing, synchronous clock-driven operation, discrete digital signals, instruction execution. This architecture is extraordinarily successful for symbolic computation, numerical calculation, and many forms of AI — including the transformers that dominate current AI