Authors: George Rajna
Researchers at UNC Charlotte and the University of Sydney have recently developed a new framework for evaluating creativity in co-creative systems in which humans and computers collaborate on creative tasks.  Vitaly Shmatikov, professor of computer science at Cornell Tech, developed models that determined with more than 90 percent accuracy whether a certain piece of information was used to train a machine learning system.  Researchers at King Abdulaziz University, in Saudi Arabia, have recently used Big Data Analytics to detect spatio-temporal events around London, testing the potential of these tools in harnessing valuable live information.  To achieve remarkable results in computer vision tasks, deep learning algorithms need to be trained on large-scale annotated datasets that include extensive informationabout every image.  Brian Mitchell and Linda Petzold, two researchers at the University of California, have recently applied model-free deep reinforcement learning to models of neural dynamics, achieving very promising results.  have come up with a novel machine learning method that enables scientists to derive insights from systems of previously intractable complexity in record time.  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.
Comments: 37 Pages.
[v1] 2018-08-13 05:56:16
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