Welcome to the Technocracy A.I. Abstract Series for Published Scientific Work in the A.I. and Artificial General Intelligence field.
Today’s paper is titled: Chip Up AI Performance
Authored By George Rajna
Abstract: Princeton researchers, in collaboration with Analog Devices Inc., have fabricated a chip that markedly boosts the performance and efficiency of neural networks
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computer algorithms modeled on the workings of the human brain. [28] “It will be interesting to see if this collection is used to train future generations of computer models,” Sturm says. [27] Now, a team of A*STAR researchers and colleagues has developed a detector that can successfully pick out where human actions will occur in videos, in almost real-time. [26] A team of researchers affiliated with several institutions in Germany and the U.S. has
developed a deep learning algorithm that can be used for motion capture of animals of any kind. [25] In 2016, when we inaugurated our new IBM Research lab in Johannesburg, we took on
this challenge and are reporting our first promising results at Health Day at the KDD
Data Science Conference in London this month. [24] The research group took advantage of a system at SLAC’s Stanford Synchrotron Radiation
Lightsource (SSRL) that combines machine learning
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a form of artificial intelligence
where computer algorithms glean knowledge from enormous amounts of data
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with
experiments that quickly make and screen hundreds of sample materials at a time. [23] Researchers at the UCLA Samueli School of Engineering have demonstrated that deep
learning, a powerful form of artificial intelligence, can discern and enhance microscopic
details in photos taken by smartphones. [22] Such are the big questions behind one of the new projects underway at the MIT-IBM
Watson AI Laboratory, a collaboration for research on the frontiers of artificial intelligence. [21] The possibility of cognitive nuclear-spin processing came to Fisher in part through studies
performed in the 1980s that reported a remarkable lithium isotope dependence on the
behavior of mother rats. [20] And as will be presented today at the 25th annual meeting of the Cognitive Neuroscience
Society (CNS), cognitive neuroscientists increasingly are using those emerging artificial
networks to enhance their understanding of one of the most elusive intelligence systems, the human brain. [19]
U.S. Army Research Laboratory scientists have discovered a way to leverage emerging
brain-like computer architectures for an age-old number-theoretic problem known as integer factorization. [18] Now researchers at the Department of Energy’s Lawrence Berkeley National Laboratory
(Berkeley Lab) and UC Berkeley have come up with a novel machine learning method that enables scientists to derive insights from systems of previously intractable complexity in record time. [17]
As always thank you for listening to the Technocracy Abstract Series and a special thank you for our sponsors the Foundation, Transhumanity.net and the AGI Laboratory.
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