And Now, a Bicycle Built for None

#artificialintelligence

"That is where we see the big promise," said Mike Davies, who oversees Intel's efforts to build neuromorphic chips. Over the past decade, the development of artificial intelligence has accelerated thanks to what are called neural networks: complex mathematical systems that can learn tasks by analyzing vast amounts of data. By metabolizing thousands of cat photos, for instance, a neural network can learn to recognize a cat. This is the technology that recognizes faces in the photos you post to Facebook, identifies the commands you bark into your smartphone and translates between languages on internet services like Microsoft Skype. It is also hastening the advance of autonomous robots, including self-driving cars.


Intel's neuro guru slams deep learning: 'it's not actually learning'

ZDNet

"Backpropogation doesn't correlate to the brain," insists Mike Davies, head of Intel's neuromorphic computing unit, dismissing one of the key tools of the species of A.I. Davies made the comment during a talk on Thursday at the International Solid State Circuits Conference in San Francisco, a prestigious annual gathering of semiconductor designers. Davies was returning fire after Facebook's Yann LeCun, a leading apostle of deep learning, earlier in the week dismissed Davies's own technology during LeCun's opening keynote for the conference. "The brain is the one example we have of truly intelligent computation," observed Davies. In contrast, so-called back-prop, invented in the 1980s, is a mathematical technique used to optimize the response of artificial neurons in a deep learning computer program. Although deep learning has proven "very effective," Davies told a ballroom of attendees, "there is no natural example of back-prop," he said, so it doesn't correspond to what one would consider real learning.


Intel's neuro guru slams deep learning: 'it's not actually learning' ZDNet

#artificialintelligence

"Backpropogation doesn't correlate to the brain," insists Mike Davies, head of Intel's neuromorphic computing unit, dismissing one of the key tools of the species of A.I. Davies made the comment during a talk on Thursday at the International Solid State Circuits Conference in San Francisco, a prestigious annual gathering of semiconductor designers. Davies was returning fire after Facebook's Yann LeCun, a leading apostle of deep learning, earlier in the week dismissed Davies's own technology during LeCun's opening keynote for the conference. "The brain is the one example we have of truly intelligent computation," observed Davies. In contrast, so-called back-prop, invented in the 1980s, is a mathematical technique used to optimize the response of artificial neurons in a deep learning computer program. Although deep learning has proven "very effective," Davies told a ballroom of attendees, "there is no natural example of back-prop," he said, so it doesn't correspond to what one would consider real learning.


International Business Machines Corp. Develops Brain-Inspired Supercomputer - Artificial Intelligence Online

#artificialintelligence

Lawrence Livermore National Laboratory (LLNL) has purchased a new brain-inspired supercomputing platform developed by International Business Machines Corp (NYSE:IBM). Based on a breakthrough neurosynaptic computer chip called IBM TrueNorth, the scalable platform will process the equivalent of 16 million neurons and 4 billion synapses while consuming only the energy equivalent of a tablet computer. The brain-like, neural network design of the IBM neuromorphic system is able to run complex cognitive tasks such as pattern recognition and integrated sensory processing far more efficiently than conventional chips. LLNL will receive a 16-chip TrueNorth system representing a total of 16 million neurons and 4 billion synapses. The new system will be used to explore new computing capabilities important to the National Nuclear Security Administration (NNSA) missions in cybersecurity, stewardship of the nation's nuclear weapons stockpile and nonproliferation.


Neuromorphic Computing Market Technology and Rising Demand For Artificial Intelligence

#artificialintelligence

Neuromorphic computing or neuromorphic engineering has been described as the use of large integration systems containing numerous analog circuits allowing the replication of neuro-biological behaviors existing in a human's nervous system. The neuromorphic computing market platform consists of two vital systems based on the custom hardware architecture. Such systems are designed to program neural microcircuits by applying brain-like thought process in cognitive computing and machine learning process. This procedure enables a machine to learn, adapt and function like a human brain does rather than functioning like a normal computer. In addition, to perform such a complex task, the computing platform requires the state-of-the-art circuit technologies and electronic components, which allows the platform to receive new data or knowledge gained from various other sources of neuroscience research, e.g.