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Biologically inspired: How neural networks are finally maturing

Joab Jackson | Dec. 19, 2013
Loosely modeled on the human brain, artificial neural networks are finally finding use in industry.

There are numerous efforts under way to try to replicate, at high fidelity, how the brain operates in hardware, such as the EU's Human Brain Project (see accompanying story: "Bringing brains to computers"). Researchers in the field of computer science, however, are borrowing the ideas from biology to build systems that, over time, may learn in the same way brains do, even if their approach differs from that of biological organisms.

Although investigated since the 1940s, research into ANNs, which can be thought of as a form of artificial intelligence (AI), hit a peak of popularity in the late 1980s.

"There was a lot of great things done as part of the neural network resurgence in the late 1980s," said Dharmendra Modha, an IBM Research senior manager who is involved in a company project to build a neuromorphic processor. Throughout the next decade, however, other forms of closely related AI started getting more attention, such as machine learning and expert systems, thanks to a more immediate applicability to industry usage.

Nonetheless, the state-of-the-art in neural networks continued to evolve, with the introduction of powerful new learning models that could be layered to sharpen performance in pattern recognition and other capabilities,

"We've come to the stage where much closer simulation of natural neural networks is possible with artificial means," Reznick said. While we still don't know entirely how the brain works, a lot of advances have been made in cognitive science, which, in turn, are influencing the models that computer scientists are using to build neural networks.

"That means that now our artificial computer models will be much closer to the way natural neural networks process information," Reznick said.

The continuing march of Moore's Law has also lent a helping hand. Over the past decade, the microprocessor fabrication process has provided the density needed to run large clusters of nodes even on a single slice of silicon, a density that would not have been possible even a decade ago.

"We're now at a point where the silicon has matured and technology nodes have gotten dense enough where it can deliver unbelievable scale at really low power," Modha said.

Reznick is leading a number of projects to harness today's processors in a neural network-like fashion. He is investigating the possibility of using GPUs (graphics processing units), which thanks to their large number of processing cores, are inherently adapt at parallel computing. He is also investigating how neural networking could improve intrusion detection systems, which are used for detect everything from trespassers on a property to malicious hackers trying to break into a computer system.

Today's intrusion detection systems work in one of two ways, Reznick explained. They either use signature detection, in which they recognize a pattern based on a pre-existing library of patterns. Or they look for anomalies in a typically static backdrop, which can be difficult to do in scenarios with lots of activity. Neural networking could combine the two approaches to strengthen the ability of the system to detect unusual deviations from the norm, Reznick said

 

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