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“In your machine-learning project, how much time will you typically spend on data preparation and transformation?” asks a 2022 Google course on the Foundations of Machine Learning (ML). The two choices offered are either “Less than half the project time” or “More than half the project time.” If you guessed the latter, you would be correct; Google states that it takes over 80 percent of project time to format the data, and that’s not even taking into account the time needed to frame the problem in machine-learning terms.

“It would take many weeks of effort to figure out the appropriate model for our dataset, and this is a really prohibitive step for a lot of folks that want to use machine learning or biology,” says Jacqueline Valeri, a fifth-year PhD student of biological engineering in Collins’s lab who is first co-author of the paper.

BioAutoMATED is an automated machine-learning system that can select and build an appropriate model for a given dataset and even take care of the laborious task of data preprocessing, whittling down a months-long process to just a few hours. Automated machine-learning (AutoML) systems are still in a relatively nascent stage of development, with current usage primarily focused on image and text recognition, but largely unused in subfields of biology, points out first co-author and Jameel Clinic postdoc Luis Soenksen PhD ‘20.

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Over centuries of painstaking laboratory work, chemists have synthesized several hundred thousand inorganic compounds — generally speaking, materials not based on the chains of carbon atoms that are characteristic of organic chemistry. Yet studies suggest that billions of relatively simple inorganic materials are still waiting to be discovered3. So where to start looking?

Many projects have tried to cut down on time spent in the lab tinkering with various materials by computationally simulating new inorganic materials and calculating properties such as how their atoms would pack together in a crystal. These efforts — including the Materials Project based at the Lawrence Berkeley National Laboratory (LBNL) in Berkeley, California — have collectively come up with about 48,000 materials that they predict will be stable.

Google DeepMind has now supersized this approach with an AI system called graph networks for materials exploration (GNoME). After training on data scraped from the Materials Project and similar databases, GNoME tweaked the composition of known materials to come up with 2.2 million potential compounds. After calculating whether these materials would be stable, and predicting their crystal structures, the system produced a final tally of 381,000 new inorganic compounds to add to the Materials Project database1.

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NASA revealed multiple new panoramic images of clouds and dust in Mars’ skies and one of its two tiny moons taken by the spacecraft last May. They were captured by the Odyssey’s camera, called the Thermal Emission Imaging System, or THEMIS.

The rare images were taken from an altitude of about 250 miles, the same altitude at which the International Space Station flies above Earth, according to NASA.

“If there were astronauts in orbit over Mars, this is the perspective they would have,” said Jonathon Hill, the operations lead of THEMIS. “No Mars spacecraft has ever had this kind of view before.”

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Applying simple, ancient weaving techniques to newly recognized properties of organic crystals, researchers with the Smart Materials Lab (SML) and the Center for Smart Engineering Materials (CSEM) at NYU Abu Dhabi (NYUAD) have, for the first time, developed a unique form of woven “textile.” These new fabric patches expand one-dimensional crystals into flexible, integrated, two-dimensional planar structures that are incredibly strong—some 20 times stronger than the original crystals—and resistant to low temperatures.

These traits give them a host of exciting potential applications, including in that range from sensing devices to optical arrays, as well as in extreme conditions such as low temperatures encountered in space exploration.

In the paper titled “Woven Organic Crystals” published in the journal Nature Communications, Panče Naumov, NYUAD Professor of Chemistry and Director of the CSEM, and colleagues from Jilin University demonstrate that organic crystal can be simply woven into flexible and robust patches with plain, twill, and satin textures.

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A lot of what we talk about with artificial intelligence and machine learning is what you might call “technical considerations” – which makes sense, because these are groundbreaking technologies.

But AI is going to be social, too – it’s going to have a social context. One way to explain that is that with ‘humans in the loop’ and assistive AI, the AI has to be able to interact with humans in particular ways.

So what about the social end of AI research?

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The new study estimates $25.7 billion lost annually in waste management and damage to marine ecosystems.

Olga355/iStock.

Cigarette filters were marketed under the guise of addressing health concerns by providing a false impression of safety. These filters, made of a material called cellulose acetate, don’t actually reduce health risks and can even harm the lungs. The cellulose acetate fibers have been shown to deposit into the lungs of smokers.

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Under the guidance of Agnieszka Pilat, the trio of Spot robot dogs will independently paint an acrylic ground canvas for their participation in the event.

Agnieszka Pilat.

The trio of Spot robot dogs developed by Boston Dynamics are scheduled to independently paint an acrylic ground canvas for their participation in the upcoming NGV Triennial, which takes place in Melbourne in December.

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