Lifeboat Foundation

By employing a neural network, the company says its numbers will be more accurate—and allow it to offer to buy more homes.

Only one photo is needed to extract the information about the font and imitate it on any other word you choose to type in.

Those are the names of the new robots Amazon is testing with the goal of reducing strenuous movements for workers.

While the introduction of robots to the workplace often raises questions about whether human jobs will be replaced, Amazon argues they simply allow workers to focus on tasks that most need their attention while minimizing their potential for injury. Amazon said it’s added over a million jobs around the world since it began using robotics in its facilities in 2012.

In May, Amazon announced a goal of reducing recordable incident rates by 50% by 2025. It plans to invest over $300 million into safety projects this year.

Mayflower will make the two-week trip from the UK to the US guided by an AI-powered captain and without humans on board.

For years, Big Tech has set the global AI research agenda. Now, groups like Black in AI and Queer in AI are upending the field’s power dynamics to build AI that serves people.

“These are novel living machines. They are not a traditional robot or a known species of animals. It is a new class of artifacts: a living and programmable organism,” says Joshua Bongard, an expert in computer science and robotics at the University of Vermont (UVM) and one of the leaders of the find.

As the scientist explains, these living bots do not look like traditional robots : they do not have shiny gears or robotic arms. Rather, they look more like a tiny blob of pink meat in motion, a biological machine that researchers say can accomplish things traditional robots cannot.

Xenobots are synthetic organisms designed automatically by a supercomputer to perform a specific task, using a process of trial and error (an evolutionary algorithm), and are built by a combination of different biological tissues.

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New footage released from the US Navy shows an unmanned drone refueling a fighter jet.

THIS is that upward exponential point that heralds the arrival of the Technological Singularity.

This is an Inside Science story.

Artificial intelligence can design computer microchips that perform at least as well as those designed by human experts, devising such blueprints thousands of times faster. This new research from Google is already helping with the design of microchips for the company’s next generation of AI computer systems.

Continue reading “Google researchers show artificial intelligence can design microchips better and faster than humans” »

A long-held goal by chemists across many industries, including energy, pharmaceuticals, energetics, food additives and organic semiconductors, is to imagine the chemical structure of a new molecule and be able to predict how it will function for a desired application. In practice, this vision is difficult, often requiring extensive laboratory work to synthesize, isolate, purify and characterize newly designed molecules to obtain the desired information.

Recently, a team of Lawrence Livermore National Laboratory (LLNL) materials and computer scientists have brought this vision to fruition for energetic molecules by creating machine learning (ML) models that can predict molecules’ crystalline properties from their chemical structures alone, such as molecular density. Predicting crystal structure descriptors (rather than the entire crystal structure) offers an efficient method to infer a material’s properties, thus expediting materials design and discovery. The research appears in the Journal of Chemical Information and Modeling.

“One of the team’s most prominent ML models is capable of predicting the crystalline density of energetic and energetic-like molecules with a high degree of accuracy compared to previous ML-based methods,” said Phan Nguyen, LLNL applied mathematician and co-first author of the paper.