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An AI-native experimental laboratory for autonomous biomolecular operations

China built an AI that runs an entire biology lab on it’s own.

Researchers from Shangha published a paper that feels like a massive leap toward artificial scientists.

They built AutoDNA, an “AI-native experimental laboratory”

Until now, if you asked an AI to design a biology experiment, it could give you a great protocol. But a human still had to walk into a lab, hold the pipettes, run the thermal cyclers, and tweak the process when things inevitably went wrong.

AutoDNA removes the human.

You just type a natural language prompt, and a swarm of specialized AI agents takes over.

Here’s how wild this multi-agent architecture is:

Continue reading “An AI-native experimental laboratory for autonomous biomolecular operations” | >

Connecting artificial intelligence with the tools of astrophysical research

Investigating a scientific question is often a complex process. As an astrophysicist studying some of the most energetic phenomena in the universe, I know that finding answers requires much more than reading scientific papers or understanding existing theories. Astrophysical research involves collecting observations, analyzing data, building physical models and comparing their predictions with observations. These tasks require different scientific tools, databases and modeling software and can take considerable time even for experienced researchers.

This challenge motivated the development of AstroGenesis, a multi-agent AI system designed to connect different stages of astrophysical research within a single environment. Our goal is to make scientific investigations faster and more accessible. A paper describing the work is posted to the arXiv preprint server.

Virtual modeling reveals safety measures for battery-powered mining systems

The mining industry is shifting toward battery-powered equipment to improve efficiency, but lithium-ion batteries pose risks due to their high energy density. They can fail, overheat and, in some cases, catch fire, releasing toxic emissions.

By combining three modeling approaches to build virtual mining systems, Penn State researchers now more comprehensively understand how to predict and mitigate lithium-ion battery fires in mines before they spread, endangering miners and halting operations.

Lung cancer: Can a new blood test warn of recurrence early on?

Now, a new blood test that detects traces of lung cancer DNA could help identify patients at risk of recurrence months before the disease becomes visible on scans. The findings may pave the way for more personalized follow-up care and treatment after surgery.

The study, published in The Journal of Molecular Diagnostics, suggests that liquid biopsies could eventually complement standard imaging and help doctors tailor follow-up care according to each patient’s risk of recurrence. However, further research is necessary to establish how best to use these tests in routine clinical practice and whether acting on their results improves long-term survival.

Ultrathin films that overcome ‘dead layer’ effect could enable energy-efficient microchips for AI

Multiferroics are a promising class of materials for making computer chips and other electronic components that can operate at much lower power than today’s devices. The materials have coupled electrical and magnetic properties that can be manipulated simultaneously with a relatively tiny electrical voltage.

Engineers are hopeful that multiferroics could help reduce the energy burden associated with the rise of AI, but there’s a problem known as the “dead layer” effect: When these materials are made thin enough to use in a computer chip, they lose the electrical and magnetic properties that make them so interesting.

Now, a team of Brown University researchers has developed a technique for making thin films of the multiferroic bismuth ferrite that retain their key properties and have some emergent properties that make them even more useful.