Lifeboat Foundation

Advances in the AI realm are constantly coming out, but they tend to be limited to a single domain: For instance, a cool new method for producing synthetic speech isn’t also a way to recognize expressions on human faces. Meta (AKA Facebook) researchers are working on something a little more versatile: an AI that can learn capably on its own whether it does so in spoken, written or visual materials.

The traditional way of training an AI model to correctly interpret something is to give it lots and lots (like millions) of labeled examples. A picture of a cat with the cat part labeled, a conversation with the speakers and words transcribed, etc. But that approach is no longer in vogue as researchers found that it was no longer feasible to manually create databases of the sizes needed to train next-gen AIs. Who wants to label 50 million cat pictures? Okay, a few people probably — but who wants to label 50 million pictures of common fruits and vegetables?

Currently some of the most promising AI systems are what are called self-supervised: models that can work from large quantities of unlabeled data, like books or video of people interacting, and build their own structured understanding of what the rules are of the system. For instance, by reading a thousand books it will learn the relative positions of words and ideas about grammatical structure without anyone telling it what objects or articles or commas are — it got it by drawing inferences from lots of examples.

Machines can now distinguish between 12 different types of plastic, thanks to a new camera system developed in Denmark.

A research team from the University of Waterloo is working on a passive treatment method that enables the removal of arsenic from surface water through a combination of waste materials.

Inspired by the growth of bones in the skeleton, researchers at the universities of Linkoping in Sweden and Okayama in Japan have developed a combination of materials that can morph into various shapes before hardening. The material is initially soft but later hardens through a bone development process that uses the same materials found in the skeleton.

When we are born, we have gaps in our skulls that are covered by pieces of soft connective tissue called fontanelles. It is thanks to fontanelles that our skulls can be deformed during birth and pass successfully through the birth canal. Post-birth, the fontanelle tissue gradually changes to hard bone. Now, researchers have combined materials that together resemble this natural process. “We want to use this for applications where materials need to have different properties at different points in time. Firstly, the material is soft and flexible, and it is then locked into place when it hardens. This material could be used in, for example, complicated bone fractures. It could also be used in microrobots — these soft microrobots could be injected into the body through a thin syringe, and then they would unfold and develop their own rigid bones”, says Edwin Jager, associate professor at the Department of Physics, Chemistry and Biology (IFM) at Linkoping University.

The idea was hatched during a research visit in Japan when materials scientist Edwin Jager met Hiroshi Kamioka and Emilio Hara, who conduct research into bones. The Japanese researchers had discovered a kind of biomolecule that could stimulate bone growth under a short period of time. Would it be possible to combine this biomolecule with Jager’s materials research, to develop new materials with variable stiffness? In the study that followed, published in Advanced Materials, the researchers constructed a kind of simple “microrobot”, one which can assume different shapes and change stiffness. The researchers began with a gel material called alginate. On one side of the gel, a polymer material is grown. This material is electroactive, and it changes its volume when a low voltage is applied, causing the microrobot to bend in a specified direction.

Most toilet models flush away waste with gallons of water. Instead, the BeeVi toilet – a portmanteau of the words’ bee’ and ‘vision’ – use a vacuum pump to suck shit into an underground bioreactor, which means it uses less water. The energy-producing toilet system is much smaller than the existing flushable toilets, as it treats human excrement without using water.

The system utilizes a natural biological process to break down human waste into a dehydrated odorless compost-like material. Once these powdered feces are transferred to the Microbial Energy Production system, they can later be converted to methane, which becomes a source of energy for the building, powering a gas stove, hot-water boiler, and solid oxide fuel cell.

“If we think out of the box, faeces has precious value to make energy and manure. I have put this value into ecological circulation,” the inventor Cho Jae-weon said.

Berkeley Lab engineers have developed an all-season smart roof coating that keeps homes warm during the winter and cool during the summer without consuming natural gas or electricity. The all-season roof coating automatically switches from keeping you cool to warm, depending on outdoor air temperature.

The problem with many cool-roof systems currently on the market is that they continue to radiate heat in the winter, which drives up heating costs, explained Junqiao Wu, a faculty scientist who led the study. “Our new material – called a temperature-adaptive radiative coating (TARC) – can enable energy savings by automatically turning off the radiative cooling in the winter, overcoming the problem of overcooling,” he said.

The key to the technology is a strange compound called vanadium dioxide (VO2). In 2017, Wu and his research team discovered that electrons in vanadium dioxide behave like metal to electricity but an insulator to heat. Below about 67 degrees Celsius, vanadium dioxide is also transparent to thermal-infrared light. But once vanadium dioxide reaches 67 degrees Celsius, the material switches to a metal state, becoming absorptive of thermal-infrared light. This ability to switch from one phase to another – in this case, from an insulator to metal – is characteristic of what’s known as a phase-change material.

The model describing a material where order doesn’t disappear as heat is applied also has implications for our understanding of the early universe.

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Efficient electrocatalysts, which are needed for the production of green hydrogen, for example, are hidden in materials composed of five or more elements. A team from Ruhr-Universität Bochum (RUB) and the University of Copenhagen has developed an efficient method for identifying promising candidates in the myriad of possible materials. To this end, the researchers combined experiments and simulation.

They published their report in the journal Advanced Energy Materials (“Unravelling Composition–Activity–Stability Trends in High Entropy Alloy Electrocatalysts by Using a Data-Guided Combinatorial Synthesis Strategy and Computational Modeling”).

A view of the sputtering machine used to produce the material library counters. (Image: Christian Nielinger)