AI Abstract Series E13 – Chip Up AI Performance

Welcome to the Technocracy A.I. Abstract Series for Published Scientific Work in the A.I. and Artificial General Intelligence field.

Todays 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

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

a form of artificial intelligence

where computer algorithms glean knowledge from enormous amounts of data


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, and the AGI Laboratory.


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