Toggle light / dark theme

In short, we’re buying so much stuff that it’s becoming hard to get that stuff where it needs to be without undue negative impacts on infrastructure, transit networks, traffic, and ultimately, quality of life. People aren’t going to stop buying stuff, so how can we plan for a future where there’s more stuff being moved and our streets aren’t overwhelmingly clogged with semis and delivery vans (and the polluting emissions that come with them)?

A Swiss company has an idea—one that’s pretty original, a bit wacky, and maybe excessive. Or maybe it’s brilliant; you be the judge.

Underground cargo is the concept. It’s also, as it were, the name: Cargo Sous Terrain. In a network of subterranean tunnels, large pods ferry pallets of goods between various hubs. The pods are automated and electric, able to pick up and drop off loads from designated points, and the network would run constantly, like a conveyor belt in a factory.

Engineers have developed a new class of smart textiles that can shape-shift and turn a two-dimensional material into 3D structures.

The team from UNSW Sydney’s Graduate School of Biomedical Engineering, and Tyree Foundation Institute of Health Engineering (Tyree iHealthE), led by Dr. Thanh Nho Do, have produced a material which is constructed from tiny soft artificial “muscles”—which are long silicon tubes filled with fluid which are manipulated to move via hydraulics.

These , which are surrounded by a helical coil of traditional fibers, can be programmed to contract or expand into a variety of shapes depending on its initial structure.

US-based intelligent automation provider, Automation Anywhere has expanded its collaboration with ICT Academy to upskill additional thousands more students from engineering and non-engineering institutes in Robotic Process Automation (RPA).

The partnership aims to complete more than 500,000 RPA-related courses over the next two years. More than 300,000 certifications have already been issued under the program, the company said.

Through this learning initiative students will access content from Automation Anywhere University (AAU). The program provides role-based learning trails, courses and certifications based on Automation 360 — the company’s AI-powered, cloud native intelligent automation platform.

The entirety of the known universe is teeming with an infinite number of molecules. But what fraction of these molecules have potential drug-like traits that can be used to develop life-saving drug treatments? Millions? Billions? Trillions? The answer: novemdecillion, or 1060. This gargantuan number prolongs the drug development process for fast-spreading diseases like COVID-19 because it is far beyond what existing drug design models can compute. To put it into perspective, the Milky Way has about 100 thousand million, or 108, stars.

In a paper that will be presented at the International Conference on Machine Learning (ICML), MIT researchers developed a geometric deep-learning model called EquiBind that is 1,200 times faster than one of the fastest existing computational molecular docking models, QuickVina2-W, in successfully binding drug-like molecules to proteins. EquiBind is based on its predecessor, EquiDock, which specializes in binding two proteins using a technique developed by the late Octavian-Eugen Ganea, a recent MIT Computer Science and Artificial Intelligence Laboratory and Abdul Latif Jameel Clinic for Machine Learning in Health (Jameel Clinic) postdoc, who also co-authored the EquiBind paper.

Before can even take place, drug researchers must find promising drug-like molecules that can bind or “dock” properly onto certain protein targets in a process known as . After successfully docking to the protein, the binding drug, also known as the ligand, can stop a protein from functioning. If this happens to an essential protein of a bacterium, it can kill the bacterium, conferring protection to the human body.

The potential of quantum computing can in no way be undermined today as it solves some of the most obstinate challenges from bringing down global warming to dramatically bringing down drug discovery time and much more. And with this, several companies are in a spree to bring up quantum computing capabilities.

Nvidia has announced a unified computing platform that will bring in an open environment across quantum processors and classical computers. The company said that the platform aims at speeding enhanced quantum research and development across Artificial Intelligence (AI), High Performance Computing (HPC), health, finance and other disciplines.

The company claims that Nvidia Quantum Optimized Device Architecture or QODA is a first-of-its-kind platform for hybrid quantum-classical computers and aims to make quantum computing more accessible by creating a comprehensive hybrid quantum-classical programming model.

Artificial intelligence (AI) systems are already far superior to humans in some tasks, such as playing Go or processing enormous amounts of data, yet even just a few months after we are born, AI is still far behind us in many other areas.

For instance, even very young kids instinctively understand that an object shouldn’t disappear and then reappearance somewhere else. Babies react with amazement when they witness such a magic trick.

TensorFlow.NET is a library that provides a. NET Standard binding for TensorFlow. It allows. NET developers to design, train and implement machine learning algorithms, including neural networks. Tensorflow. NET also allows us to leverage various machine learning models and access the programming resources offered by TensorFlow.

TensorFlow

TensorFlow is an open-source framework developed by Google scientists and engineers for numerical computing. It is composed by a set of tools for designing, training and fine-tuning neural networks. TensorFlow’s flexible architecture makes it possible to deploy calculations on one or more processors (CPUs) or graphics cards (GPUs) on a personal computer, server, without re-writing code.

Two independent groups have experimentally demonstrated surface-code quantum error correction—an approach for remedying errors in quantum computations.


The small robotic crab can walk, bend, twist, turn and jump The smallest-ever remote-controlled walking robot has been created by Northwestern University engineers, and it takes the shape of a tiny, cute peekytoe crab. The tiny crabs, which are about half a millimeter wide, can bend, twist, craw.