Authors: George Rajna
A team of researchers from Shanghai Jiao Tong University and the University of Science and Technology of China has developed a chip that allows for two-dimensional quantum walks of single photons on a physical device.  The physicists, Sally Shrapnel, Fabio Costa, and Gerard Milburn, at The University of Queensland in Australia, have published a paper on the new quantum probability rule in the New Journal of Physics.  Probabilistic computing will allow future systems to comprehend and compute with uncertainties inherent in natural data, which will enable us to build computers capable of understanding, predicting and decision-making.  For years, the people developing artificial intelligence drew inspiration from what was known about the human brain, and it has enjoyed a lot of success as a result. Now, AI is starting to return the favor.  Scientists at the National Center for Supercomputing Applications (NCSA), located at the University of Illinois at Urbana-Champaign, have pioneered the use of GPU-accelerated deep learning for rapid detection and characterization of gravitational waves.  Researchers from Queen Mary University of London have developed a mathematical model for the emergence of innovations.  Quantum computers can be made to utilize effects such as quantum coherence and entanglement to accelerate machine learning.  Neural networks learn how to carry out certain tasks by analyzing large amounts of data displayed to them.  Who is the better experimentalist, a human or a robot? When it comes to exploring synthetic and crystallization conditions for inorganic gigantic molecules, actively learning machines are clearly ahead, as demonstrated by British Scientists in an experiment with polyoxometalates published in the journal Angewandte Chemie.  Machine learning algorithms are designed to improve as they encounter more data, making them a versatile technology for understanding large sets of photos such as those accessible from Google Images. Elizabeth Holm, professor of materials science and engineering at Carnegie Mellon University, is leveraging this technology to better understand the enormous number of research images accumulated in the field of materials science. 
Comments: 34 Pages.
[v1] 2018-05-14 09:51:57
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