Via: Network World:
The military’s advanced research group recently put out a call, or Request For information, on how it could develop systems that go beyond machine learning, Bayesian techniques, and graphical technology to solve “extraordinarily difficult recognition problems in real-time.”
Current systems offer partial solutions to this problem, but are limited in their ability to efficiently scale to larger more complex datasets, DARPA said. “They are also compute intensive, exhibit limited parallelism, require high precision arithmetic, and, in most cases, do not account for temporal data. ”
What DARPA is interested in is looking at mimicking a portion of the brain known as the neocortex which is utilized in higher brain functions such as sensory perception, motor commands, spatial reasoning, conscious thought and language. Specifically, DARPA said it is looking for information that provides new concepts and technologies for developing what it calls a “Cortical Processor” based on Hierarchical Temporal Memory.
“Although a thorough understanding of how the cortex works is beyond current state of the art, we are at a point where some basic algorithmic principles are being identified and merged into machine learning and neural network techniques. Algorithms inspired by neural models, in particular neocortex, can recognize complex spatial and temporal patterns and can adapt to changing environments. Consequently, these algorithms are a promising approach to data stream filtering and processing and have the potential for providing new levels of performance and capabilities for a range of data recognition problems,” DARPA stated. “The cortical computational model should be fault tolerant to gaps in data, massively parallel, extremely power efficient, and highly scalable. It should also have minimal arithmetic precision requirements, and allow ultra-dense, low power implementations.”
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