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Category: robotics/AI

Researchers at the Nanoscience Center and at the Faculty of Information Technology at the University of Jyväskylä in Finland have demonstrated that new distance-based machine learning methods developed at the University of Jyväskylä are capable of predicting structures and atomic dynamics of nanoparticles reliably. The new methods are significantly faster than traditional simulation methods used for nanoparticle research and will facilitate more efficient explorations of particle-particle reactions and particles’ functionality in their environment. The study was published in a Special Issue devoted to machine learning in the Journal of Physical Chemistry on May 15, 2020.

The new methods were applied to ligand-stabilized metal , which have been long studied at the Nanoscience Center at the University of Jyväskylä. Last year, the researchers published a method that is able to successfully predict binding sites of the stabilizing ligand molecules on the nanoparticle surface. Now, a new tool was created that can reliably predict based on the atomic structure of the particle, without the need to use numerically heavy electronic structure computations. The tool facilitates Monte Carlo simulations of the atom dynamics of the particles at elevated temperatures.

Potential energy of a system is a fundamental quantity in computational nanoscience, since it allows for quantitative evaluations of system’s stability, rates of chemical reactions and strengths of interatomic bonds. Ligand-stabilized metal nanoparticles have many types of interatomic bonds of varying chemical strength, and traditionally the energy evaluations have been done by using the so-called density functional theory (DFT) that often results in numerically heavy computations requiring the use of supercomputers. This has precluded efficient simulations to understand nanoparticles’ functionalities, e.g., as catalysts, or interactions with biological objects such as proteins, viruses, or DNA. Machine learning methods, once trained to model the systems reliably, can speed up the simulations by several orders of magnitude.

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Plug And Play

The underlying mechanics of a quantum computer won’t be any less difficult to comprehend under Gil’s vision of the future. But, he argues, it won’t matter because programming quantum computing software would become far more automated along the way.

“You’ll simply have to write a line of code in any programming language you work with,” Gil wrote, “and the system will match it with the circuit in the library and the right quantum computer.”

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Here’s What You Need To Remember: Chinese so-called “carrier-killer” missiles could, quite possibly, push a carrier back to a point where its fighters no longer have range to strike inland enemy targets from the air. The new drone is being engineered, at least in large measure, as a specific way to address this problem. If the attack distance of an F-18, which might have a combat radius of 500 miles or so, can double — then carrier-based fighters can strike targets as far as 1000 miles away if they are refueled from the air.

The Navy will choose a new carrier-launched drone at the end of this year as part of a plan to massively expand fighter jet attack range and power projection ability of aircraft carriers.

The emerging Navy MQ-25 Stingray program, to enter service in the mid-2020s, will bring a new generation of technology by engineering a first-of-its-kind unmanned re-fueler for the carrier air wing.

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DeepMind wowed the research community several years ago by defeating grandmasters in the ancient game of Go, and more recently saw its self-taught agents thrash pros in the video game StarCraft II. Now, the UK-based AI company has delivered another impressive innovation, this time in text-to-speech (TTS).

Text-to-speech (TTS) systems take natural language text as input and produce synthetic human-like speech as their output. The text-to-speech synthesis pipelines are complex, comprising multiple processing stages such as text normalisation, aligned linguistic featurisation, mel-spectrogram synthesis, raw audio waveform synthesis and so on.

Although contemporary TTS systems like those used in digital assistants like Siri boast high-fidelity speech synthesis and wide real-world deployment, even the best of them still have drawbacks. Each stage requires expensive “ground truth” annotations to supervise the outputs, and the systems cannot train directly from characters or phonemes as input to synthesize speech in the end-to-end manner increasingly favoured in other machine learning domains.

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A team of researchers at Université de Sherbrooke with assistance from a group at Exonetik Inc., has created a wearable supernumerary robotic arm that adds functionality for common human tasks. In their paper published in IEEE Spectrum, the group describes their robotic arm, its abilities and their plans for expanding its functionality.

A supernumerary robotic device is of a type that adds functionality to an existing system. In this case, the team in Canada added a third arm and associated three-fingered hand to a human subject.

The bottom part of the is strapped to the waist and hips of the user, anchoring it in place. The robot arm extends from its anchor and performs functions as the user either watches or carries out his or her own activities. The arm is motivated by magnetorheological clutches and hydrostatic transmissions, giving it three degrees of freedom. It does not weigh much, either, just four kilograms. But that is because it has a tethered power/control source that remains on the ground. It also is remotely controlled by another person standing nearby. Future plans call for semi-autonomous control.

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IBM will no longer offer general purpose facial recognition or analysis software, IBM CEO Arvind Krishna said in a letter to Congress today. The company will also no longer develop or research the technology, IBM tells The Verge. Krishna addressed the letter to Sens. Cory Booker (D-NJ) and Kamala Harris (D-CA) and Reps. Karen Bass (D-CA), Hakeem Jeffries (D-NY), and Jerrold Nadler (D-NY).

“IBM firmly opposes and will not condone uses of any [facial recognition] technology, including facial recognition technology offered by other vendors, for mass surveillance, racial profiling, violations of basic human rights and freedoms, or any purpose which is not consistent with our values and Principles of Trust and Transparency,” Krishna said in the letter. “We believe now is the time to begin a national dialogue on whether and how facial recognition technology should be employed by domestic law enforcement agencies.”

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MIT engineers have designed a “brain-on-a-chip,” smaller than a piece of confetti, that is made from tens of thousands of artificial brain synapses known as memristors—silicon-based components that mimic the information-transmitting synapses in the human brain.

The researchers borrowed from principles of metallurgy to fabricate each memristor from alloys of silver and copper, along with silicon. When they ran the chip through several , the chip was able to “remember” stored images and reproduce them many times over, in versions that were crisper and cleaner compared with existing memristor designs made with unalloyed elements.

Their results, published today in the journal Nature Nanotechnology, demonstrate a promising new memristor design for neuromorphic devices—electronics that are based on a new type of circuit that processes information in a way that mimics the brain’s neural architecture. Such brain-inspired circuits could be built into small, , and would carry out complex computational tasks that only today’s supercomputers can handle.

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We’ve always had a soft spot for supernumerary robotic limbs here at The Verge, but this latest example of the genre is one of the most impressive we’ve seen to date. Designed by researchers at the Université de Sherbrooke in Canada, it’s a hydraulic arm that sits on the wearer’s hip and uses a three-fingered manipulator to carry out a range of tasks.

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