AI Writ Small

AI writ small or a nanowire sized neural net that learns presages a new age of artificial intelligence at truly small scale.

Brains, beyond their signature achievements in thinking and problem solving, are paragons of energy efficiency. The human brain’s power consumption resembles that of a 20-watt incandescent lightbulb. In contrast, one of the world’s largest and fastest supercomputers, the K computer in Kobe, Japan, consumes as much as 9.89 megawatts of energy — an amount roughly equivalent to the power usage of 10,000 households. Yet in 2013, even with that much power, it took the machine 40 minutes to simulate just a single second’s worth of 1 percent of human brain activity.

Now engineering researchers at the California NanoSystems Institute at the University of California, Los Angeles, are hoping to match some of the brain’s computational and energy efficiency with systems that mirror the brain’s structure. They are building a device, perhaps the first one, that is “inspired by the brain to generate the properties that enable the brain to do what it does,” according to Adam Stieg, a research scientist and associate director of the institute, who leads the project with Jim Gimzewski, a professor of chemistry at UCLA.

The device is a far cry from conventional computers, which are based on minute wires imprinted on silicon chips in highly ordered patterns. The current pilot version is a 2-millimeter-by-2-millimeter mesh of silver nanowires connected by artificial synapses. Unlike silicon circuitry, with its geometric precision, this device is messy, like “a highly interconnected plate of noodles,” Stieg said. And instead of being designed, the fine structure of the UCLA device essentially organized itself out of random chemical and electrical processes.

This network of highly interconnected nanowires may look chaotic and random, but its structure and behavior resemble those of neurons in the brain. Researchers at the California NanoSystems Institute are developing it as a brainlike device for learning and computation.

The more powerful systems become, the more biological they resemble - Robert E.