Another foundation patent in the area of machine learning
Albuquerque, NM / http://www.myprgenie.com/ / ACCESSWIRE / May 22, 2014 / KnowmTech, LLC today announced that it has received a Notice of Allowance from the United States Patent and Trademark Office (USPTO) for U.S. Patent Application Serial No. 12/974,829 entitled, "Framework for the Evolution of Electronic Neural Assemblies Toward Directed Goals." This patent broadly covers a framework for the self-organization of electronic neural assemblies toward directed goals including a system or framework for the evolution of extrinsic logic states.
This is another key patent for KnowmTech, LLC in the area of machine learning.
About KnowmTech, LLC. ( www.knowmtech.com)
KnowmTech, LLC was founded in 2002 to develop breakthrough machine learning technologies that address fundamental problems inherent in modern computing architectures. The separation of memory and processing in computing architectures leads to wasted energy shuttling information back and forth. This causes tremendous problems for simulations of adaptive operations-exactly the operations needed for things like machine learning (ML). Since ML is inherently about adaptation, it turns out that building large-scale adaptive systems like brains-at a power efficiency close to biology-is simply not feasible with traditional computing approaches. Current super-computing installations consume the equivalent of a "Three-Gorges Dam's worth"-over 10 Gwatts-to emulate one human-scale brain at low resolution in real-time.
AHaH computing changes this by taking advantage of a universal adaptive building block found throughout nature and mapping this to electronic circuits. The physical circuit turns out to be exceedingly simple, allowing for various levels of simulation abstraction - including a very efficient digital emulator that runs on modern hardware. A first prototype of a KnowmTM-based neural processing unit (NPU) called Thermodynamic RAM is currently being pursued with development partners this summer.
"We are building this from the bottom up" says Inventor Alex Nugent. "We started with nano-particles, went to memristors, then synapses and neurons. Now we have machine learning modules across the domains of perception, planning and control. The pieces have really come together."
Contact: Hillary Riggs, firstname.lastname@example.org, 505-988-7016
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