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Blog – Page 11794

As achievements go, learning how to pick up objects doesn’t sound quite as impressive as twice beating the world Go champion – it is, after all, something the average toddler can do. But it’s the fact that the robots themselves figured out the best way to do it using neural networks that makes this notable.

A recent Google report spotted by TNW explains how the company let robot arms pick up a variety of different objects, using neural networks to learn by trial-and-error the best way to handle each. Some 800,000 goes later, the robots seemed to have it figured out pretty well …

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More urgency placed on making Microfluidics/ embedded H2O droplets for cooling microchips so that the emergence of high performing microchips coming in the future.

DARPA and Lockheed Martin have a plan to build microfluidic cooling into modern microprocessors. This could dramatically improve CPU cooling and break the bottleneck on clock speed scaling — at least, for a little while.

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Government’s other big NextGen Program “Advanced Research Projects Agency-EnergyAdvanced Research Projects Agency-Energy” (ARPA) is funding a personal climate change solution with robots, foot coolers, etc. There is one fact; US Government does love their acronyms.

Why heat or cool a whole building when you could heat or cool individual people instead?

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More large steps forward in Quantum technology with the latest chip with optical qubits.

The optical chip overcomes a number of obstacles in the development of quantum computers. A research team has demonstrated that on-chip quantum frequency combs can be used to simultaneously generate multiphoton entangled quantum bit states. It is the first chip capable of simultaneously generating multi-photon qubit states and two-photon entangled states on hundreds of frequency modes. The chip is scalable, compact, and compatible with existing technologies.

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Allen Institute working with Baylor on reconstructing neuronal connections.

The Intelligence Advanced Research Projects Activity (IARPA) has awarded an $18.7 million contract to the Allen Institute for Brain Science, as part of a larger project with Baylor College of Medicine and Princeton University, to create the largest ever roadmap to understand how the function of networks in the brain’s cortex relates to the underlying connections of its individual neurons.

The project is part of the Machine Intelligence from Cortical Networks (MICrONS) program, which seeks to revolutionize machine learning by reverse-engineering the algorithms of the brain.

“This effort will be the first time that we can physically look at more than a thousand connections between neurons in a single cortical network and understand how those connections might allow the network to perform functions, like process visual information or store memories,” says R. Clay Reid, Ph.D., Senior Investigator at the Allen Institute for Brain Science, Principal Investigator on the project.

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