In a development that may enable a wholly new approach to artificial intelligence, researchers at Harvard University's School of Engineering and Applied Sciences (SEAS) have invented a type of transistor that can learn in ways similar to a neural synapse. Called a synaptic transistor, the new device self-optimizes its properties for the functions it has carried out in the past.
One of the more remarkable features of the human brain is it gets better at whatever it does. While your first day on an assembly line may be full of fumbling and confusion, in a week or two you will find yourself seemingly on autopilot, performing the set of required tasks without much mental effort. After a few months, you will respond automatically when a part comes through damaged or improperly oriented. Plasticity is the name for the brain's ability to change its own structure through thought and activity.
Most of this plasticity results from changes in the 100 trillion or so synapses, or interconnections, between brain cells. One of the ways through which sets of behaviors are reinforced, or learned, is called spike-timing dependent plasticity, or STDP.
Often summed up by the aphorism "Cells that fire together, wire together", when neuron A repeatedly sends a signal across a synapse that causes neuron B to fire, the synapse will strengthen, in effect making that decision easier to make in the future.
The synaptic transistor could mark the beginning of a new kind of artificial intelligence: one embedded not in smart algorithms but in the very architecture of a computer. In principle, a system integrating millions of tiny synaptic transistors and neuron terminals could take parallel computing into a new era of ultra-efficient high performance.
"This kind of proof-of-concept demonstration carries that work into the 'applied' world," says research team leader Professor Shriram Ramanathan, "where you can really translate these exotic electronic properties into compelling, state-of-the-art devices." Hopefully those SOTA devices can someday be assembled into SOTA learning machines.
Source: Gizmag
