Tag Archives: computer

New Material For New Processor

Computers used to take up entire rooms. Today, a two-pound laptop can slide effortlessly into a backpack. But that wouldn’t have been possible without the creation of new, smaller processors — which are only possible with the innovation of new materials. But how do materials scientists actually invent new materials? Through experimentation, explains Sanket Deshmukh, an assistant professor in the chemical engineering department of Virginia Tech whose team’s recently published computational research might vastly improve the efficiency and costs savings of the material design process.

Deshmukh’s lab, the Computational Design of Hybrid Materials lab, is devoted to understanding and simulating the ways molecules move and interact — crucial to creating a new material. In recent years, materials scientists have employed machine learning, a powerful subset of artificial intelligence, to accelerate the discovery of new materials through computer simulations. Deshmukh and his team have recently published research in the Journal of Physical Chemistry Letters demonstrating a novel machine learning framework that trainson the fly,” meaning it instantaneously processes data and learns from it to accelerate the development of computational models. Traditionally the development of computational models are “carried out manually via trial-and-error approach, which is very expensive and inefficient, and is a labor-intensive task,” Deshmukh explained.

This novel framework not only uses the machine learning in a unique fashion for the first time,” Deshmukh said, “but it also dramatically accelerates the development of accurate computational models of materials.” “We train the machine learning model in a ‘reverse’ fashion by using the properties of a model obtained from molecular dynamics simulations as an input for the machine learning model, and using the input parameters used in molecular dynamics simulations as an output for the machine learning model,” said Karteek Bejagam, a post-doctoral researcher in Deshmukh’s lab and one of the lead authors of the study.

This new framework allows researchers to perform optimization of computational models, at unusually faster speed, until they reach the desired properties of a new material.

Source: https://vtnews.vt.edu/

NanoComputers Could Run a Million Times Faster

Transition metal dichalcogenides (TMDCs) possess optical properties that could be used to make computers run a million times faster and store information a million times more energy-efficiently, according to a study led by Georgia State University.

Computers operate on the time scale of a fraction of a nanosecond, but the researchers suggest constructing computers on the basis of TMDCs, atomically thin semiconductors, could make them run on the femtosecond time scale, a million times faster. This would also increase computer memory speed by a millionfold.

There is nothing faster, except light,” said Dr. Mark Stockman, lead author of the study and director of the Center for Nano-Optics and a Regents’ Professor in the Department of Physics and Astronomy at Georgia State. “The only way to build much faster computers is to use optics, not electronics. Electronics, which is used by current computers, can’t go any faster, which is why engineers have been increasing the number of processors. We propose the TMDCs to make computers a million times more efficient. This is a fundamentally different approach to information technology.”

The researchers propose a theory that TMDCs have the potential to process information within a couple of femtoseconds. A femtosecond is one millionth of one billionth of a second. A TMDC has a hexagonal lattice structure that consists of a layer of transition metal atoms sandwiched between two layers of chalcogen atoms. This hexagonal structure aids in the computer processor speed and also enables more efficient information storage. The TMDCs have a number of positive qualities, including being stable, non-toxic, thin, light and mechanically strong. Examples include molybdenum disulfide (MOS2) and tungsten diselenide (WSe2). TMDCs are part of a large family called 2D materials, which is named after their extraordinary thinness of one or a few atoms. In this study, the researchers also established the optical properties of the TMDCs, which allow them to be ultrafast.

The findings are published in the journal Physical Review B.

Source: https://news.gsu.edu/

AI creates 3D ‘digital heart’ to aid patient diagnoses

Armed with a mouse and computer screen instead of a scalpel and operating theater, cardiologist Benjamin Meder carefully places the electrodes of a pacemaker in a beating, digital heart.  Using this “digital twin” that mimics the electrical and physical properties of the cells in patient 7497’s heart, Meder runs simulations to see if the pacemaker can keep the congestive heart failure sufferer alivebefore he has inserted a knife.

A three-dimensional printout of a human heart is seen at the Heidelberg University Hospital (Universitaetsklinikum Heidelberg)

The digital heart twin developed by Siemens Healthineers, a German company is one example of how medical device makers are using artificial intelligence (AI) to help doctors make more precise diagnoses as medicine enters an increasingly personalized age.

The challenge for Siemens Healthineers and rivals such as Philips and GE Healthcare is to keep an edge over tech giants from Alphabet’s Google to Alibaba that hope to use big data to grab a slice of healthcare spending.

With healthcare budgets under increasing pressure, AI tools such as the digital heart twin could save tens of thousands of dollars by predicting outcomes and avoiding unnecessary surgery.

Source: https://www.healthcare.siemens.com/
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https://www.reuters.com/

Teaching a car how to drive itself in 20 minutes

Researchers from Wayve, a company founded by a team from the Cambridge University engineering department, have developed a neural network sophisticated enough to learn how to drive a car in 15 to 20 minutes using nothing but a computer and a single camera. The company showed off its robust deep learning methods last week in a company blog post showcasing the no-frills approach to driverless car development. Where companies like Waymo and Uber are relying on a variety of sensors and custom-built hardware, Wayve is creating the world’s first autonomous vehicles based entirely on reinforcement learning.

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The AI powering Wayve’s self-driving system is remarkable for its simplicity. It’s a four layer convolutional neural network (learn about neural networks here) that performs all of its processing on a GPU inside the car. It doesn’t require any cloud connectivity or use pre-loaded mapsWayve’s vehicles are early-stage level five autonomous. There’s a lot of work to be done before Wayve’s AI can drive any car under any circumstances. But the idea that driverless cars will require tens of thousands of dollars worth of extraneous hardware is taking a serious blow in the wake of the company’s amazing deep learning techniques. According to Wayve, these algorithms are only going to get smarter.

Source: https://wayve.ai/
AND
https://thenextweb.com/

Human Internal Verbalizations Understood Instantly By Computers

MIT researchers have developed a computer interface that can transcribe words that the user verbalizes internally but does not actually speak aloud. The system consists of a wearable device and an associated computing system. Electrodes in the device pick up neuromuscular signals in the jaw and face that are triggered by internal verbalizations — saying wordsin your head” — but are undetectable to the human eye. The signals are fed to a machine-learning system that has been trained to correlate particular signals with particular words. The device also includes a pair of bone-conduction headphones, which transmit vibrations through the bones of the face to the inner ear. Because they don’t obstruct the ear canal, the headphones enable the system to convey information to the user without interrupting conversation or otherwise interfering with the user’s auditory experience.

The device is thus part of a complete silent-computing system that lets the user undetectably pose and receive answers to difficult computational problems. In one of the researchers’ experiments, for instance, subjects used the system to silently report opponents’ moves in a chess game and just as silently receive computer-recommended responses.

The motivation for this was to build an IA device — an intelligence-augmentation device,” says Arnav Kapur, a graduate student at the MIT Media Lab, who led the development of the new system. “Our idea was: Could we have a computing platform that’s more internal, that melds human and machine in some ways and that feels like an internal extension of our own cognition?” “We basically can’t live without our cellphones, our digital devices,” adds Pattie Maes, a professor of media arts and sciences and Kapur’s thesis advisor. “But at the moment, the use of those devices is very disruptive. If I want to look something up that’s relevant to a conversation I’m having, I have to find my phone and type in the passcode and open an app and type in some search keyword, and the whole thing requires that I completely shift attention from my environment and the people that I’m with to the phone itself. So, my students and I have for a very long time been experimenting with new form factors and new types of experience that enable people to still benefit from all the wonderful knowledge and services that these devices give us, but do it in a way that lets them remain in the present.”

Source: http://news.mit.edu/