Authors: George Rajna
A new wheelchair may give people with severe mobility challenges another reason to smile about artificial intelligence—that grin might literally help them control their wheelchair.  Now, researchers at Stanford University have devised a new type of artificially intelligent camera system that can classify images faster and more energy efficiently, and that could one day be built small enough to be embedded in the devices themselves, something that is not possible today.  Today, deep neural networks with different architectures, such as convolutional, recurrent and autoencoder networks, are becoming an increasingly popular area of research.  A deep neural network running on an ordinary desktop computer is interpreting highly technical data related to national security as well as—and sometimes better than— today's best automated methods or even human experts.  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] 2018-12-04 10:06:16
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