Scientific Reports –a crucial aspect of language acquisition. Prior experimental studies proved that artificial grammars can be learnt by human subjects after little exposure and often without explicit knowledge of the underlying rules. We tested four grammars with different complexity levels both in humans and in feedforward and recurrent networks. Our results show that both architectures can “learn” (via error back-propagation) the grammars after the same number of training sequences as humans do, but recurrent networks perform closer to humans than feedforward ones, irrespective of the grammar complexity level. Moreover, similar to visual processing, in which feedforward and recurrent architectures have been related to unconscious and conscious processes, the difference in performance between architectures over ten regular grammars shows that simpler and more explicit grammars are better learnt by recurrent architectures, supporting the hypothesis that explicit learning is best modeled by recurrent networks, whereas feedforward networks supposedly capture the dynamics involved in implicit learning.

Herbert Ong Brighter with Herbert.

“Our research in the past decade has made analog memristor a viable technology,” said Dr. Qiangfei Xia. “It is time to move such a great technology into the semiconductor industry to benefit the broad AI hardware community.”

Digital computing has become the norm in our everyday lives, but their limits are being reached in terms of computing power. Can analog computing step in and outperform them? This is what a recent study published in Science hopes to address as a team of researchers from the University of Southern California, TetraMem Inc., and the University of Massachusetts, Amherst (UMass Amherst) have spent the last decade developing memristors, which are capable of overcoming the computing limits of digital computing. This study holds the potential to help researchers develop more efficient methods in storing data without the drawbacks of holding too much of it, thus creating a clog.

“In this work, we propose and demonstrate a new circuit architecture and programming protocol that can efficiently represent high-precision numbers using a weighted sum of multiple, relatively low-precision analog devices, such as memristors, with a greatly reduced overhead in circuitry, energy and latency compared with existing quantization approaches,” said Dr. Qiangfei Xia, who is a professor of Electrical & Computer Engineering at UMass Amherst and a co-author on the study.