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Category: robotics/AI

Geothermal Could Power 65 Million U.S. Homes by 2050, DOE Says

Somewhat ironically, the technological breakthroughs that make this veritable holy grail of clean energy possible are largely borrowed from the oil and gas industry. The hydraulic fracturing industry has made leaps and bounds when it comes to advancing and refining drilling technologies, and a workforce with a fracking background has therefore been instrumental in making geothermal more feasible and cost-effective.

For example, Mike Matson, the CEO and cofounder of a startup called Birch Geothermal, is applying his background in drilling and reservoir management in the oil and gas industry and applying that expertise directly to geothermal energy innovation. “Birch plans to make use of sensors and autonomous systems to better control how water moves through geothermal wells, ensuring that heat remains steady for reliable electricity generation,” Forbes recently wrote in a profile of Birch Geothermal. “The team is also focused on optimizing reservoir design using techniques originally developed for the oil and gas industry.”

This marriage of clean energy outlooks with fossil fuel expertise gives the United States a major opportunity to become a world leader in enhanced geothermal. “The U.S. has a number of different superpowers and putting holes in the ground and taking things out of those holes is one of them — and doing so more economically and more efficiently than basically any other place on Earth,” Drew Nelson, vice president of Project InnerSpace, was quoted by Cipher News.

Preventing the next pandemic using AI-designed vaccines

For most of human history, infectious diseases were the main causes of morbidity and mortality. Advances in sanitation, antibiotics, vaccines, and public health dramatically shifted that balance, particularly in high-income countries, where life expectancy has increased by nearly 40 years over the past century. Yet the COVID-19 pandemic provided a stark reminder that infectious threats can still reshape societies almost overnight. Between 2019 and 2021 alone, life expectancy in the US fell by more than two years, and recent modelling suggests there is roughly a 50 percent chance of another COVID-scale pandemic occurring within the next 25 years.

Historically, the vaccine development model has been largely reactive and variant-driven, but the industry is now actively shifting toward proactive and universal vaccinology to get ahead of evolving pathogens. Recent results from a first-in-human clinical trial led by the University of Cambridge and its spin-out DIOSynVax, published in the Journal of Infection, provide early clinical evidence of this shift, demonstrating the safety of an AI-designed “super-antigen” intended to provide broad viral coverage.

Teaching AI to Invent Enzymes Nature Never Imagined

Evolution is an extraordinary engine for enzymatic diversity, yet the chemistry it has explored remains a narrow slice of what DNA can encode. Deep generative models can design new proteins that bind ligands, but none have created enzymes without pre-specifying catalytic residues.

In this webinar, Chenghao Liu and Jarrid Brooks from the Arnold Lab at Caltech will introduce DISCO (DIffusion for Sequence-structure CO-design). This multimodal model co-designs protein sequence and 3D structure around arbitrary biomolecules, as well as inference-time scaling methods that optimize objectives across both modalities. Conditioned solely on reactive intermediates, DISCO designs diverse heme enzymes with novel active-site geometries. These enzymes catalyze new-to-nature carbene-transfer reactions, including alkene cyclopropanation, spirocyclopropanation, B-H, and C(sp^3)-H insertions, with high activities exceeding those of engineered enzymes. Random mutagenesis of a selected design further confirmed that enzyme activity can be improved through directed evolution. By providing a scalable route to evolvable enzymes, DISCO broadens the potential scope of genetically encodable transformations.

Matlab-deep-learning/pose-estimation-3D-with-stereo-camera: This demo uses a deep neural network and two generic cameras to perform 3D pose estimation

This demo uses a deep neural network and two generic cameras to perform 3D pose estimation. — matlab-deep-learning/pose-estimation-3D-with-stereo-camera

Agentic AI bot helps scientists speak to robots, speeding up experiments

Researchers at the Department of Energy’s Pacific Northwest National Laboratory use a slew of autonomous robots to design and implement experiments. However, setting up an experiment on an autonomous lab robot is surprisingly slow. The effort requires a lengthy back-and-forth between a scientist and an engineer to design the experimental steps—a process that can take weeks.

To help researchers work more efficiently, a PNNL team developed a generative agentic AI that can quickly translate experimental goals into instructions for a laboratory robot. The translation agent, called AutoLabs, is currently designed to operate with Big Kahuna, an automated robot built by Unchained Labs that researchers use to study new and existing battery materials. The system can carry out multistep experimental workflows, including mixing, heating, stirring and filtering samples with minimal human intervention. By automating these processes, researchers can perform five to 10 times more experiments than would be practical by hand.

The team published a paper in Scientific Reports about AutoLabs, and the software is also available for other researchers to download on GitHub.

Inorganic nanoscale device behaves like a single neuron, opening doors for AI and retinal implants

McGill University researchers have developed a light-detecting nanoscale structure that mimics how a neuron processes information. The neuron-like behavior emerges from the materials themselves, reducing the energy demand associated with similar devices that rely on circuits or software.

Instead of capturing data first and processing it elsewhere, the device senses and interprets light in the same place, similar to how the eye processes visual information.

The researchers say the discovery could increase the efficiency of vision-based technologies like artificial retinas and smart optical sensors. It could also transform how artificial neural networks (ANNs), a foundation of machine learning, are built. The research is published in the journal Nanoscale.

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