http://www.ibm.com/blogs/think/2016/01/13/ibms-cognitive-computing-innovation-surges/
IBM’s Cognitive Computing Innovation Surges
IBM’s work in cognitive computing emerged as a major factor among the patents awarded to the company by the US. Patent and Trademark Office. More than 800 of IBM’s 7,355 2015 patents are related to cognitive computing, a 52% increase from the previous year.
Last year, IBM announced that it has realigned its entire business strategy around the promise of cognitive computers, which can learn, reason, and efficiently process diverse data types all while interacting with people in natural and familiar ways.
IBM’s leaders believe cognitive computing will have a profound impact on business and society, helping people to make better decisions in their personal and professional lives, democratizing expertise, and transforming industries and professions.
Simply put, IBM’s innovation agenda calls for developing cognitive solutions delivered via a cloud platform to transform industries.
Here are some highlights among last year’s cognitive computing innovations:
Creating machines that mimic the brain: Conventional computers are good at math, but they can’t tell the difference between a tree and a truck—at least, not without using a truckload of computing power. A invented a new kind of processor chip that draws inspiration from the structure and function of the human brain while exploiting creative possibilities in modern technology — to learn and adapt on the fly, and is 10,000 times more energy efficient than conventional chips. (Patent US8977583) ((cites our patent. see below for all citations))
http://www.google.com.au/patents/US20100076916#legal-events
BrainChip:
Autonomous Learning Dynamic Artificial Neural Computing Device and Brain Inspired System
US 20100076916 A1
ABSTRACT
A hierarchical information processing system is disclosed having a plurality of artificial neurons, comprised of binary logic gates, and interconnected through a second plurality of dynamic artificial synapses, intended to simulate or extend the function of a biological nervous system. The system is capable of approximation, autonomous learning and strengthening of formerly learned input patterns. The system learns by simulated Synaptic Time Dependent Plasticity, commonly abbreviated to STDP. Each artificial neuron consisting of a soma circuit and a plurality of synapse circuits, whereby the soma membrane potential, the soma threshold value, the synapse strength and the Post Synaptic Potential at each synapse are expressed as values in binary registers, which are dynamically determined from certain aspects of input pulse timing, previous strength value and output pulse feedback...
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Title |
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31 May 2011 |
29 Apr 2014 |
International Business Machines Corporation |
Structural plasticity in spiking neural networks with symmetric dual of an electronic neuron |
| 2 |
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29 Jul 2011 |
23 Sep 2014 |
International Business Machines Corporation |
Hierarchical routing for two-way information flow and structural plasticity in neural networks |
| 3 |
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29 Mar 2012 |
21 Oct 2014 |
International Business Machines Coproration |
Multi-compartment neurons with neural cores |
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18 Nov 2014 |
Qualcomm Incorporated |
Methods and systems for neural processor training by encouragement of correct output |
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29 Feb 2012 |
9 Dec 2014 |
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Multi-compartment neuron suitable for implementation in a distributed hardware model by reducing communication bandwidth |
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8 Mar 2012 |
17 Feb 2015 |
Hrl Laboratories, Llc |
Spike timing dependent plasticity apparatus, system and method |
| 8 |
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29 Mar 2012 |
10 Mar 2015 |
International Business Machines Corporation |
Synaptic, dendritic, somatic, and axonal plasticity in a network of neural cores using a plastic multi-stage crossbar switching |
| 9 |
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27 Jan 2012 |
31 Mar 2015 |
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Hierarchical scalable neuromorphic synaptronic system for synaptic and structural plasticity |
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21 Jul 2011 |
9 Jun 2015 |
Qualcomm Incorporated |
Method and apparatus of robust neural temporal coding, learning and cell recruitments for memory using oscillation |
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14 Jun 2012 |
23 Jun 2015 |
Qualcomm Incorporated |
Learning spike timing precision |
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21 Jul 2015 |
Samsung Electronics Co., Ltd. |
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28 Jul 2015 |
Qualcomm Incorporated |
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Qualcomm Incorporated |
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7 Jul 2010 |
8 Sep 2015 |
Qualcomm Incorporated |
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16 Aug 2011 |
29 Sep 2015 |
Qualcomm Incorporated |
Method and apparatus for neural temporal coding, learning and recognition |
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8 Aug 2012 |
10 Nov 2015 |
International Business Machines Corporation |
Structural plasticity in spiking neural networks with symmetric dual of an electronic neuron |
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24 Mar 2014 |
17 Nov 2015 |
International Business Machines Corporation |
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19 Oct 2011 |
25 Apr 2013 |
Qualcomm Incorporated |
Method and apparatus for neural learning of natural multi-spike trains in spiking neural networks |
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27 Jan 2012 |
1 Aug 2013 |
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Multi-compartment neuron suitable for implementation in a distributed hardware model by reducing communication bandwidth |
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8 Jul 2011 |
12 Jan 2012 |
Qualcomm Incorporated |
Methods and systems for neural processor training by encouragement of correct output |
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19 Oct 2012 |
25 Apr 2013 |
Qualcomm Incorporated |
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7 Feb 2013 |
15 Aug 2013 |
Qualcomm Incorporated |
Methods and apparatus for spiking neural computation |
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