Circa 2011 o,.o.
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Intel’s fifth-generation Loihi chip uses neuromorphic computing to learn faster on less training data than traditional artificial intelligence techniques — including how to smell like a human does and make accurate conclusions based on a tiny dataset of essentially just one sample.
“That’s really one of the main things we’re trying to understand and map into silicon … the brain’s ability to learn with single examples,” Mike Davies, the director of Intel’s Neuromorphic Computing Lab, told me recently on The AI Show podcast. “So with just showing one clean presentation of an odor, we can store that in this high dimensional representation in the chip, and then it allows it to then recognize a variety of noisy, corrupted, occluded odors like you would be faced with in the real world.”
A team of researchers at Heidelberg University has succeeded in building an apparatus that allowed them to observe Pauli crystals for the first time. They have written a paper describing their efforts and have uploaded it to the arXiv preprint server.
The Pauli exclusion principle is quite simple: It asserts that no two fermions can have the same quantum number. But as with many principles in physics, this simple assertion has had a profound impact on quantum mechanics. Looking more closely at the principle reveals that it also suggests that no two fermions can occupy the same quantum state. And that means that electrons must have different orbits around a nucleus, and by extension, it explains why atoms have volume. This understanding of the self-ordering of fermions has led to other findings—for instance, that they should form crystals with a specific geometry, which are now known as Pauli crystals. When this observation was first made, it was understood that such crystal formation could only happen under unique circumstances. In this new effort, the researchers have resolved the circumstances, and in so doing, have built an apparatus that allowed them to observe Pauli crystals for the first time.
The work involved a setup that included lasers that were able to trap a cloud of lithium-6 atoms supercooled to their lower energy state, forcing them to adhere to the exclusion principle, in a one-atom thick flat layer. The team then used a technique that allowed them to photograph the atoms when they were in a particular given state—and only those atoms. They then used the camera to take 20,000 pictures, but used only those that showed the right number of atoms—-indicating that they were adhering to the Pauli exclusion principle. Next, the team processed the remaining images to remove the impact of overall momentum in the atom cloud, rotated them properly, and then superimposed thousands of them, revealing the momentum distribution of the individual atoms —that was the point at which crystal structures began to emerge in the photographs, just as was predicted by theory. The researchers note that their technique could also be used to study other effects related to fermion-based gases.
It sounds like a riddle: What do you get if you take two small diamonds, put a small magnetic crystal between them and squeeze them together very slowly?
The answer is a magnetic liquid, which seems counterintuitive. Liquids become solids under pressure, but not generally the other way around. But this unusual pivotal discovery, unveiled by a team of researchers working at the Advanced Photon Source (APS), a U.S. Department of Energy (DOE) Office of Science User Facility at DOE’s Argonne National Laboratory, may provide scientists with new insight into high-temperature superconductivity and quantum computing.
Though scientists and engineers have been making use of superconducting materials for decades, the exact process by which high-temperature superconductors conduct electricity without resistance remains a quantum mechanical mystery. The telltale signs of a superconductor are a loss of resistance and a loss of magnetism. High-temperature superconductors can operate at temperatures above those of liquid nitrogen (−320 degrees Fahrenheit), making them attractive for lossless transmission lines in power grids and other applications in the energy sector.
In her new column covering neuromorphic engineering, intelligent robotics, and AI hardware, Sunny Bains looks at attempts to increase connectivity by creating three dimensional systems.
A real-time deep-learning model is proposed to classify the volume of cuttings from a shale shaker on an offshore drilling rig by analyzing the real-time monitoring video stream. As opposed to the traditional, time-consuming video-analytics method, the proposed model can implement a real-time classification and achieve remarkable accuracy. The approach is composed of three modules. Compared with results manually labeled by engineers, the model can achieve highly accurate results in real time without dropping frames.
Introduction
A complete work flow already exists to guide the maintenance and cleaning of the borehole for many oil and gas companies. A well-formulated work flow helps support well integrity and reduce drilling risks and costs. One traditional method needs human observation of cuttings at the shale shaker and a hydraulic and torque-and-drag model; the operation includes a number of cleanup cycles. This continuous manual monitoring of the cuttings volume at the shale shaker becomes the bottleneck of the traditional work flow and is unable to provide a consistent evaluation of the hole-cleaning condition because the human labor cannot be available consistently, and the torque-and-drag operation is discrete, containing a break between two cycles.
Modeling and rendering of dynamic scenes is challenging, as natural scenes often contain complex phenomena such as thin structures, evolving topology, translucency, scattering, occlusion, and biological motion. Mesh-based reconstruction and tracking often fail in these cases, and other approaches (e.g., light field video) typically rely on constrained viewing conditions, which limit interactivity. We circumvent these difficulties by presenting a learning-based approach to representing dynamic objects inspired by the integral projection model used in tomographic imaging. The approach is supervised directly from 2D images in a multi-view capture setting and does not require explicit reconstruction or tracking of the object. Our method has two primary components: an encoder-decoder network that transforms input images into a 3D volume representation, and a differentiable ray-marching operation that enables end-to-end training. By virtue of its 3D representation, our construction extrapolates better to novel viewpoints compared to screen-space rendering techniques. The encoder-decoder architecture learns a latent representation of a dynamic scene that enables us to produce novel content sequences not seen during training. To overcome memory limitations of voxel-based representations, we learn a dynamic irregular grid structure implemented with a warp field during ray-marching. This structure greatly improves the apparent resolution and reduces grid-like artifacts and jagged motion. Finally, we demonstrate how to incorporate surface-based representations into our volumetric-learning framework for applications where the highest resolution is required, using facial performance capture as a case in point.
Video Player
This study also analyzes the market status, market share, growth rate, future trends, market drivers, opportunities and challenges, risks and entry barriers, sales channels, distributors and Porter’s Five Forces Analysis. Neural Network Software market report all-inclusively estimates general market conditions, the growth prospects in the market, possible restrictions, significant industry trends, market size, market share, sales volume and future trends. The report starts by an introduction about the company profiling and a comprehensive review about the future events, sales strategies, Investments, business marketing strategy, future products, new geographical markets, customer actions or behaviors with the help of 100+ market data Tables, Pie Charts, Graphs & Figures spread through Pages for easy understanding. Neural Network Software market report has been designed by keeping in mind the customer requirements which assist them in increasing their return on investment (ROI and this research also provides a deep insight into the activities of key players such as Starmind, NeuralWare, Slagkryssaren AB, AND Corporation, Slashdot Media, XENON Systems Pty Ltd, Xilinx Inc and others. and others.
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Global neural network software market is set to witness a healthy CAGR of 35.70% in the forecast period of 2019 to 2026.
Computer systems that can automatically generate image captions have been around for several years. While many of these techniques perform considerably well, the captions they produce are typically generic and somewhat uninteresting, containing simple descriptions such as “a dog is barking” or “a man is sitting on a bench.”
Alasdair Tran, Alexander Mathews and Lexing Xie at the Australian National University have been trying to develop new systems that can generate more sophisticated and descriptive image captions. In a paper recently pre-published on arXiv, they introduced an automatic captioning system for news images that takes the general context behind an image into account while generating new captions. The goal of their study was to enable the creation of captions that are more detailed and more closely resemble those written by humans.
“We want to go beyond merely describing the obvious and boring visual details of an image,” Xie told TechXplore. “Our lab has already done work that makes image captions sentimental and romantic, and this work is a continuation on a different dimension. In this new direction, we wanted to focus on the context.”
A team of researchers with members from Lawrence Livermore National Laboratory, Wright-Patterson Air Force Base and the Barnes Group Advisors found that controlling spatter during laser-powder bed fusion can reduce defects in metal-based 3D printing. In their paper published in the journal Science, the group describes studying the additive manufacturing printing methodology and what they learned about it. Andrew Polonsky and Tresa Pollock with the University of California, Santa Barbara have published a Perspective piece on the work done by the team in the same journal issue.
As additive manufacturing printing methodologies mature, new materials are being tested to find out if they might be used in 3D printers to create new products. In recent years, this has extended to metals. One such technique is called laser-powder bed fusion (L-PBF). It involves the use of a high-powered laser to melt and fuse metallic powders layer by layer to produce a 3D part. It has been hoped that the technique could eventually be used for aerospace and biomedical applications. But thus far, such efforts have fallen short due to the large number of defects that occur with the process. In this new effort, the researchers have discovered a way to reduce such defects, perhaps paving the way for the technique to finally fulfill its promise.
To better understand why the L-PBF process leads to so many defects (such as undesired pores) the researchers conducted X-ray synchrotron experiments and built predictive multi-physics models to gain a better understanding of what occurs during printing. One of their goals was to better understand how energy is absorbed during printing with powder layers that are only a few particles thick.
