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Wednesday, May 18, 2016

Recreating Science Experiments Using AI: Fast, Scalable, Repeatable & Downloadable

AI Can Recreate Nobel-Prize Winning Experiments

Now the team wants to make a bigger version.

Timothy J. Seppala | May 17, 2016

We've seen how artificial intelligence has made quantum experimentation easier, and now machine learning is being implemented in other areas of scientific experimentation. A team of researchers from the Australian National University, University of Adelaide and the University of South Wales Australian Defence Force Academy (phew) used an algorithm to recreate a Nobel Prize-winning experiment that created a Bose-Einstein condensate. In simpler terms, the physicists made ultra-cold gas (1 microkelvin, less than "a billionth of a degree above absolute zero"), and then let the AI take over the rest of the experiment.

"Paul Wigley, left, and Michael Hush." Source:

<more at; related articles and links: (Artificial intelligence replaces physicists. May 16, 2016) and (Fast machine-learning online optimization of ultra-cold-atom experiments. P. B. Wigley, P. J. Everitt, A. van den Hengel, J. W. Bastian, M. A. Sooriyabandara, G. D. McDonald, K. S. Hardman, C. D. Quinlivan, P. Manju, C. C. N. Kuhn, I. R. Petersen, A. N. Luiten, J. J. Hope, N. P. Robins and M. R. Hush. Scientific Reports 6, Article number: 25890 (2016). doi:10.1038/srep25890. [Abstract: We apply an online optimization process based on machine learning to the production of Bose-Einstein condensates (BEC). BEC is typically created with an exponential evaporation ramp that is optimal for ergodic dynamics with two-body s-wave interactions and no other loss rates, but likely sub-optimal for real experiments. Through repeated machine-controlled scientific experimentation and observations our ‘learner’ discovers an optimal evaporation ramp for BEC production. In contrast to previous work, our learner uses a Gaussian process to develop a statistical model of the relationship between the parameters it controls and the quality of the BEC produced. We demonstrate that the Gaussian process machine learner is able to discover a ramp that produces high quality BECs in 10 times fewer iterations than a previously used online optimization technique. Furthermore, we show the internal model developed can be used to determine which parameters are essential in BEC creation and which are unimportant, providing insight into the optimization process of the system.])>

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