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Category: robotics/AI

Someday, we might be able to carry around tiny, AI brains that can function without supercomputers, the internet or the cloud. Researchers from MIT say their new “brain-on-a-chip” design gets us one step closer to that future. A group of engineers put tens of thousands of artificial brain synapses, known as memristors, on a single chip that’s smaller than a piece of confetti.

In a paper published in Nature Nanotechnology, the researchers explain how their brain-inspired chip was able to remember and recreate a gray-scale image of Captain America’s shield and reliably alter an image of MIT’s Killian Court by sharpening and blurring it. Those tests may seem minor, but the team believes the chip design could advance the development of small, portable AI devices and carry out complex computational tasks that today only supercomputers are capable of.

“So far, artificial synapse networks exist as software. We’re trying to build real neural network hardware for portable artificial intelligence systems,” says Jeehwan Kim, associate professor of mechanical engineering at MIT. “Imagine connecting a neuromorphic device to a camera on your car, and having it recognize lights and objects and make a decision immediately, without having to connect to the internet.”

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Researchers from the University of California San Diego (UCSD) have developed flexible feet that can help robots walk up to 40% faster on uneven terrain such as pebbles and wood chips. The work has applications for search-and-rescue missions, as well as space exploration.

“Robots need to be able to walk fast and efficiently on natural, uneven terrain, so they can go everywhere humans can, but maybe shouldn’t,” said Emily Lathrop, the study’s first author and a PhD student in the Jacobs School of Engineering at UCSD.

The researchers are presenting their breakthrough at the RoboSoft conference, taking place virtually from now until 15th July.

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Construction is one of the oldest professions as people have been building shelters and structures for millennia. However the industry has evolved quite a bit in the way they design, plan, and build structures. For decades, technology has been used in the construction industry to make jobs more efficient and construction projects and structures safer.

In recent years, construction companies have increasingly started using AI in a range of ways to make construction more efficient and innovative. From optimizing work schedules to improving workplace safety to keeping a secure watch on construction facilities, AI in the construction industry is already proving its value.

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Recently, OpenAI collaborated with UberAI to propose a new approach — Synthetic Petri Dish — for accelerating the most expensive step of Neural Architecture Search (NAS). The researchers explored whether the computational efficiency of NAS can be improved by creating a new kind of surrogate, one that can benefit from miniaturised training and still generalise beyond the observed distribution of ground-truth evaluations.

Deep neural networks have been witnessing success and are able to mitigate various business challenges such as speech recognition, image recognition, machine translation, among others for a few years now.

According to the researchers, Neural Architecture Search (NAS) explores a large space of architectural motifs and is a compute-intensive process that often involves ground-truth evaluation of each motif by instantiating it within a large network, and training and evaluating the network with thousands or more data samples. By motif, the researchers meant the design of a repeating recurrent cell or activation function that is repeated often in a larger Neural Network blueprint.

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No one can say whether androids will dream of electric sheep, but they will almost certainly need periods of rest that offer benefits similar to those that sleep provides to living brains, according to new research from Los Alamos National Laboratory.

“We study spiking , which are systems that learn much as living brains do,” said Los Alamos National Laboratory computer scientist Yijing Watkins. “We were fascinated by the prospect of training a neuromorphic processor in a manner analogous to how humans and other biological systems learn from their environment during childhood development.”

Watkins and her research team found that the simulations became unstable after continuous periods of unsupervised learning. When they exposed the networks to states that are analogous to the waves that living brains experience during sleep, stability was restored. “It was as though we were giving the neural networks the equivalent of a good night’s rest,” said Watkins.

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It’s always exciting when you can bridge two different physical concepts that seem to have nothing in common—and it’s even more thrilling when the results have as broad a range of possible fields of application as from fault-tolerant quantum computation to quantum gravity.

Physicists love to draw connections between distinct ideas, interconnecting concepts and theories to uncover new structure in the landscape of scientific knowledge. Put together information theory with quantum mechanics and you’ve opened a whole new field of quantum information theory. More recently, machine learning tools have been combined with many-body physics to find new ways to identify phases of matter, and ideas from quantum computing were applied to Pozner molecules to obtain new plausible models of how the brain might work.

In a recent contribution, my collaborators and I took a shot at combining the two physical concepts of quantum error correction and physical symmetries. What can we say about a quantum error-correcting code that conforms to a physical symmetry? Surprisingly, a continuous symmetry prevents the code from doing its job: A code can conform well to the symmetry, or it can correct against errors accurately, but it cannot do both simultaneously.

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Can artificial intelligence create great music?

Your answer, of course, will very much depend on what you call great music. Plus on when and where you’re playing it. I’ve found an AI-generated music app that creates great music for at least one purpose.

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