Reframe Systems, the Massachusetts-based physical AI company innovating homebuilding with robotic microfactories, has partnered with Cabot, Cabot & Forbes (CC&F), a specialist in transit-oriented development for Greater Boston’s new economy workers, to deploy a Reframe unit as the on-site marketing and sales office for The Bolt, a 180,000 SF innovation and manufacturing center currently under […]
DNA robots are emerging as tiny programmable machines that could one day deliver drugs, hunt viruses, and build molecular-scale devices. By borrowing ideas from traditional robotics and combining them with DNA folding techniques, scientists are creating structures that can move and act with precision. These robots can be guided using chemical reactions or external signals like light and magnetic fields.
AI’s growing energy use sounds alarming, but its global climate impact may be far smaller than expected. Researchers found that while AI consumes huge amounts of electricity, it barely moves the needle on overall emissions. The real impact is more localized, especially around data centers. Meanwhile, AI could become a powerful tool for building greener technologies.
Choosing the right method for multimodal AI—systems that combine text, images, and more—has long been trial and error. Emory physicists created a unifying mathematical framework that shows many AI techniques rely on the same core idea: compress data while preserving what’s most predictive. Their “control knob” approach helps researchers design better algorithms, use less data, and avoid wasted computing power. The team believes it could pave the way for more accurate, efficient, and environmentally friendly AI.
Researchers tested whether generative AI could handle complex medical datasets as well as human experts. In some cases, the AI matched or outperformed teams that had spent months building prediction models. By generating usable analytical code from precise prompts, the systems dramatically reduced the time needed to process health data. The findings hint at a future where AI helps scientists move faster from data to discovery.
Scientists at the University of New Hampshire have unleashed artificial intelligence to dramatically speed up the hunt for next-generation magnetic materials. By building a massive, searchable database of 67,573 magnetic compounds — including 25 newly recognized materials that stay magnetic even at high temperatures — the team is opening the door to cheaper, more sustainable technologies.
Quantum computers need extreme cold to work, but the very systems that keep them cold also create noise that can destroy fragile quantum information. Scientists in Sweden have now flipped that problem on its head by building a tiny quantum refrigerator that actually uses noise to drive cooling instead of fighting it. By carefully steering heat at unimaginably small scales, the device can act as a refrigerator, heat engine, or energy amplifier inside quantum circuits.
Princeton researchers found that the brain excels at learning because it reuses modular “cognitive blocks” across many tasks. Monkeys switching between visual categorization challenges revealed that the prefrontal cortex assembles these blocks like Legos to create new behaviors. This flexibility explains why humans learn quickly while AI models often forget old skills. The insights may help build better AI and new clinical treatments for impaired cognitive adaptability.
UMass Amherst engineers have built an artificial neuron powered by bacterial protein nanowires that functions like a real one, but at extremely low voltage. This allows for seamless communication with biological cells and drastically improved energy efficiency. The discovery could lead to bio-inspired computers and wearable electronics that no longer need power-hungry amplifiers. Future applications may include sensors powered by sweat or devices that harvest electricity from thin air.
HydroSpread, a breakthrough fabrication method, lets scientists build ultrathin soft robots directly on water. These tiny, insect-inspired machines could transform robotics, healthcare, and environmental monitoring.
A new AI tool called DOLPHIN exposes hidden genetic markers inside single cells, enabling earlier detection and more precise treatment choices. It also sets the stage for building virtual models of cells to simulate disease and drug responses.
Using laser light instead of traditional mechanics, researchers have built micro-gears that can spin, shift direction, and even power tiny machines. These breakthroughs could soon lead to revolutionary medical tools working at the scale of cells.