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University Alliance for Low Carbon Energy   Three universities, including Tsinghua University, University of Cambridge, and the Massachusetts Institute of Technology, have fostered up an alliance on November 15, 2009 to advocate low carbon energy and climate change adaptation The alliance will mainly work on 6 major areas: clean coal technology and CCS, homebuilding energy efficiency, industrial energy efficiency and sustainable transport, biomass energy and other renewable energy, advanced nuclear energy, intelligent power grid, and energy policies/planning. A steering panel made up of the senior experts from the three universities (two from each) will be established to review, evaluate, and endorse the goals, projects, fund raising activities, and collaborations under the alliance. With the Headquarters at the campus of Tsinghua University and branch offices at other two universities, the alliance will be chaired by a scientist selected from Tsinghua University.   According to a briefing, the alliance will need a budget of USD 3-5 million, mainly from the donations of government, industry, and all walks of life. In this context, the R&D findings derived from the alliance will find its applications in improving people's life.

that is pretty impressive actually

An new element for electronic circuits?

check the video under showreel on the bottom ...

wow ... look at this!

A new manufacturing process could mean improved efficiency for solar cells. The technique, which is being pioneered by researchers at the University of California, San Diego, results in "hairy" solar cells.

Where Carlo will give his award lecture.

impressive, particularly if it works like it does in the video the whole time. paper here http://mi.eng.cam.ac.uk/~qp202/

Well, impressive indeed... have to try it out...

nice tool ... but apparently not open source :-(

already posted further down the page

Genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning. For paper see: https://arxiv.org/pdf/1712.06567.pdf

Interesting pointers in this one! I would like to explore neuroevolution as well, although it seems extremely resource-demanding?

Not necessarily, I think it can be made to be much faster hybridizing it with backprop and Taylor maps. Its one ideas in the closet we still have not explored (Differential Intelligence: accelerating neuroevolution).

Fascinating insight into the question of how networks learn.