Two teams found different ways for quantum computers to process nonlinear systems by first disguising them as linear ones.
Scientists at DGIST in Korea, and UC Irvine and UC San Diego in the US, have developed a computer architecture that processes unsupervised machine learning algorithms faster, while consuming significantly less energy than state-of-the-art graphics processing units. The key is processing data where it is stored in computer memory and in an all-digital format. The researchers presented the new architecture, called DUAL, at the 2020 53rd Annual IEEE/ACM International Symposium on Microarchitecture.
“Today’s computer applications generate a large amount of data that needs to be processed by machine learning algorithms,” says Yeseong Kim of Daegu Gyeongbuk Institute of Science and Technology (DGIST), who led the effort.
Powerful “unsupervised” machine learning involves training an algorithm to recognize patterns in large datasets without providing labeled examples for comparison. One popular approach is a clustering algorithm, which groups similar data into different classes. These algorithms are used for a wide variety of data analyzes, such as identifying fake news on social media, filtering spam email and detecting criminal or fraudulent activity online.
Proteins are essential to cells, carrying out complex tasks and catalyzing chemical reactions. Scientists and engineers have long sought to harness this power by designing artificial proteins that can perform new tasks, like treat disease, capture carbon or harvest energy, but many of the processes designed to create such proteins are slow and complex, with a high failure rate.
In a breakthrough that could have implications across the healthcare, agriculture, and energy sectors, a team lead by researchers in the Pritzker School of Molecular Engineering at the University of Chicago has developed an artificial intelligence-led process that uses big data to design new proteins.
By developing machine-learning models that can review protein information culled from genome databases, the researchers found relatively simple design rules for building artificial proteins. When the team constructed these artificial proteins in the lab, they found that they performed chemical processes so well that they rivaled those found in nature.
Scientists at Freie Universität Berlin develop a deep learning method to solve a fundamental problem in quantum chemistry.
A team of scientists at Freie Universität Berlin has developed an artificial intelligence (AI) method for calculating the ground state of the Schrödinger equation in quantum chemistry. The goal of quantum chemistry is to predict chemical and physical properties of molecules based solely on the arrangement of their atoms in space, avoiding the need for resource-intensive and time-consuming laboratory experiments. In principle, this can be achieved by solving the Schrödinger equation, but in practice this is extremely difficult.
Up to now, it has been impossible to find an exact solution for arbitrary molecules that can be efficiently computed. But the team at Freie Universität has developed a deep learning method that can achieve an unprecedented combination of accuracy and computational efficiency. AI has transformed many technological and scientific areas, from computer vision to materials science. “We believe that our approach may significantly impact the future of quantum chemistry,” says Professor Frank Noé, who led the team effort. The results were published in the reputed journal Nature Chemistry.
Digital data storage is a growing need for our society and finding alternative solutions than those based on silicon or magnetic tapes is a challenge in the era of “big data.” The recent development of polymers that can store information at the molecular level has opened up new opportunities for ultrahigh density data storage, long-term archival, anticounterfeiting systems, and molecular cryptography. However, synthetic informational polymers are so far only deciphered by tandem mass spectrometry. In comparison, nanopore technology can be faster, cheaper, nondestructive and provide detection at the single-molecule level; moreover, it can be massively parallelized and miniaturized in portable devices. Here, we demonstrate the ability of engineered aerolysin nanopores to accurately read, with single-bit resolution, the digital information encoded in tailored informational polymers alone and in mixed samples, without compromising information density. These findings open promising possibilities to develop writing-reading technologies to process digital data using a biological-inspired platform.
DNA has evolved to store genetic information in living systems; therefore, it was naturally proposed to be similarly used as a support for data storage (1–3), given its high-information density and long-term storage with respect to existing technologies based on silicon and magnetic tapes. Alternatively, synthetic informational polymers have also been described (5–9) as a promising approach allowing digital storage. In these polymers, information is stored in a controlled monomer sequence, a strategy that is also used by nature in genetic material. In both cases, single-molecule data writing is achieved mainly by stepwise chemical synthesis (3, 10, 11), although enzymatic approaches have also been reported (12). While most of the progress in this area has been made with DNA, which was an obvious starting choice, the molecular structure of DNA is set by biological function, and therefore, there is little space for optimization and innovation.
With the rapid advancement of humanoid robots in the market today, we’re able to see how our lives have become simpler and easier. In this video let’s look at some of the most advanced humanoid robots that are being developed by various companies and organisations.
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“FUJIFILM Corporation (President: Kenji Sukeno) is pleased to announce that it has achieved the world’s record 317 Gbpsi recording density with magnetic tapes using a new magnetic particle called Strontium Ferrite (SrFe)*4. The record was achieved in tape running test, conducted jointly with IBM Research. This represents the development of epoch-making technology that can produce data cartridges with the capacity of 580TB (terabytes), approximately 50 times greater than the capacity of current cartridges*5. The capacity of 580TB is enough to store data equivalent to 120000 DVDs.”
TOKYO, December 162020 — FUJIFILM Corporation (President: Kenji Sukeno) is pleased to announce that it has achieved the world’s record 317 Gbpsi recording density with magnetic tapes using a new magnetic particle called Strontium Ferrite (SrFe) *4. The record was achieved in tape running test, conducted jointly with IBM Research. This represents the development of epoch-making technology that can produce data cartridges with the capacity of 580TB (terabytes), approximately 50 times greater than the capacity of current cartridges *5. The capacity of 580TB is enough to store data equivalent to 120000 DVDs.
SrFe is a magnetic material that has very high magnetic properties and is stable to maintain high performance even when processed into fine particles. It is widely used as a raw material for producing magnets for motors. Fujifilm has applied its proprietary technology to successfully develop ultra-fine SrFe magnetic particles, which can be used as a magnetic material for producing particulate magnetic tape media for data storage. The company has been conducting R&D for commercial use of SrFe magnetic particles as potential replacement of Barium Ferrite (BaFe) magnetic particles, currently used in magnetic tape data storage media. Magnetic tapes used in this test have been produced at the company’s existing coating facility, confirming the ability to support mass production and commercialization.
As the number of devices connected to the internet continues to increase, so does the amount of redundant data transfer between different sensory terminals and computing units. Computing approaches that intervene in the vicinity of or inside sensory networks could help to process this growing amount of data more efficiently, decreasing power consumption and potentially reducing the transfer of redundant data between sensing and processing units.
Researchers at Hong Kong Polytechnic University have recently carried out a study outlining the concept of near-sensor and in-sensor computing. These are two computing approaches that enable the partial transfer of computation tasks to sensory terminals, which could reduce power consumption and increase the performance of algorithms.
“The number of sensory nodes on the Internet of Things continues to increase rapidly,” Yang Chai, one of the researchers who carried out the study, told TechXplore. “By 2032, the number of sensors will be up to 45 trillion, and the generated information from sensory nodes is equivalent to 1020 bit/second. It is thus becoming necessary to shift part of the computation tasks from cloud computing centers to edge devices in order to reduce energy consumption and time delay, saving communication bandwidth and enhancing data security and privacy.”
Proof at the nexus of pure mathematics and algorithms puts ‘quantum weirdness’ on a whole new level.
While many self-driving vehicles have achieved remarkable performance in simulations or initial trials, when tested on real streets, they are often unable to adapt their trajectories or movements based on those of other vehicles or agents in their surroundings. This is particularly true in situations that require a certain degree of negotiation, for instance, at intersections or on streets with multiple lanes.
Researchers at Stanford University recently created LUCIDGames, a computational technique that can predict and plan adaptive trajectories for autonomous vehicles. This technique, presented in a paper pre-published on arXiv, integrates an algorithm based on game theory and an estimation method.
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