“We can make all sorts of assumptions about the way technology is changing learning, but what does the science actually say? According to Alfred Spector, Google’s vice president of research, it says a lot. For example, virtual tutors have helped average students reach the top 2% of their course; video games provide immersive environments that take the bordedom out of studying; and social networks are being used to increase interaction between students.”

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Margot Krasojevic‘s latest proposal, a mesh shelter that takes the concept of snow caves and applies it to an artificial structure, is built for an eminently practical purpose: a built emergency shelter for climbers and others caught in extreme conditions.

The elaborate, high tech and naturally contoured structure is as much a thought experiment as it is a serious architectural proposal, with the carbon fibre mesh acting as a snow-catcher, forming a frame for a large snow drift. The captured snow works as both building material and insulation, allowing for the creation of a shelter of several rooms. Inside sits a wooden frame suspended from the mesh and attached to the landscape by climbing ropes, which avoid freezing by swaying. This frame can have canvases attached to it, and contains cell-like modules that would act as sleeping areas…

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A team of astronomers and computer scientists at the University of Hertfordshire have taught a machine to “see” astronomical images, using data from the Hubble Space Telescope Frontier Fields set of images of distant clusters of galaxies that contain several different types of galaxies. The technique, which uses a form of AI called unsupervised machine learning, allows galaxies to be automatically classified at high speed, something previously done by thousands of human volunteers in projects like Galaxy Zoo.


“We have not told the machine what to look for in the images, but instead taught it how to ‘see,’” said graduate student Alex Hocking. “Our aim is to deploy this tool on the next generation of giant imaging surveys where no human, or even group of humans, could closely inspect every piece of data. But this algorithm has a huge number of applications far beyond astronomy, and investigating these applications will be our next step,” said University of Hertfordshire Royal Society University Research Fellow James Geach, PhD.


The scientists are now looking for collaborators to make use of the technique in applications like medicine, where it could for example help doctors to spot tumors, and in security, to find suspicious items in airport scans.

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