In what’s been hailed as a significant step forward in the development of artificial intelligence (AI), researchers at UC Santa Barbara have developed a simple neural circuit able to perform human-like image recognition tasks.
Made up of around 100 artificial synapses, the circuit was able to successfully classify three letters (“z”, “v” and “n”) by their images, each letter stylized in different ways or saturated with “noise”.
According to the team the circuitry may eventually be expanded and scaled to approach something like the human brain’s, which has 1015 (one quadrillion) synaptic connections.
Key to this technology is the memristor (a combination of “memory” and “resistor”), an electronic component whose resistance changes depending on the direction of the flow of the electrical charge.
Unlike conventional transistors, which rely on the drift and diffusion of electrons and their holes through semiconducting material, memristor operation is based on ionic movement, similar to the way human neural cells generate neural electrical signals.
“The memory state is stored as a specific concentration profile of defects that can be moved back and forth within the memristor,” said Dmitri Strukov, a professor of electrical and computer engineering. The ionic memory mechanism brings several advantages over purely electron-based memories, which makes it very attractive for artificial neural network implementation, he added.
To be able to approach functionality of the human brain, however, many more memristors would be required to build more complex neural networks to do the same kinds of things we can do with barely any effort and energy, such as identify different versions of the same thing or infer the presence or identity of an object not based on the object itself but on other things in a scene.