Australian researchers have created building blocks out of DNA to construct a series of nano-scale objects and shapes, from a rod and a square to an infinitesimally small dinosaur.
The approach turns DNA into a modular material for building nanostructures – thousands of times narrower than a human hair. Developed by researchers from the University of Sydney Nano Institute and published in the journal Science Robotics, it suggests exciting possibilities for future use of nanobot technology.
A quiet revolution is brewing in labs around the world, where scientists’ use of AI is growing exponentially. One in three postdocs now use large language models to help carry out literature reviews, coding, and editing. In October, the creators of our AlphaFold 2 system, Demis Hassabis and John Jumper became Nobel Laureates in Chemistry for using AI to predict the structure of proteins, alongside the scientist David Baker, for his work to design new proteins. Society will soon start to feel these benefits more direct ly, with drugs and materials designed with the help of AI currently making their way through development.
In this essay, we take a tour of how AI is transforming scientific disciplines from genomics to computer science to weather forecasting. Some scientists are training their own AI models, while others are fine-tuning existing AI models, or using these models’ predictions to accelerate their research. Scientists are using AI as a scientific instrument to help tackle important problems, such as designing proteins that bind more tightly to disease targets, but are also gradually transforming how science itself is practised.
There is a growing imperative behind scientists’ embrace of AI. In recent decades, scientists have continued to deliver consequential advances, from Covid-19 vaccines to renewable energy. But it takes an ever larger number of researchers to make these breakthroughs, and to transform them into downstream applications. As a result, even though the scientific workforce has grown significantly over the past half-century, rising more than seven fold in the US alone, the societal progress that we would expect to follow, has slowed. For instance, much of the world has witnessed a sustained slowdown in productivity growth that is undermining the quality of public services. Progress towards the 2030 Sustainable Development Goals, which capture the biggest challenges in health, the environment, and beyond, is stalling.
A tiny, four-fingered “hand” folded from a single piece of DNA can pick up the virus that causes COVID-19 for highly sensitive rapid detection and can even block viral particles from entering cells to infect them, University of Illinois Urbana-Champaign researchers report. Dubbed the NanoGripper, the nanorobotic hand also could be programmed to interact with other viruses or to recognize cell surface markers for targeted drug delivery, such as for cancer treatment.
Led by Xing Wang, a professor of bioengineering and of chemistry at the U. of I., the researchers describe their advance in the journal Science Robotics.
Inspired by the gripping power of the human hand and bird claws, the researchers designed the NanoGripper with four bendable fingers and a palm, all in one nanostructure folded from a single piece of DNA. Each finger has three joints, like a human finger, and the angle and degree of bending are determined by the design on the DNA scaffold.
Researchers at the University of Sydney Nano Institute have made a significant advance in the field of molecular robotics by developing custom-designed and programmable nanostructures using DNA origami.
This innovative approach has potential across a range of applications, from targeted drug delivery systems to responsive materials and energy-efficient optical signal processing. The method uses “DNA origami,” so-called as it uses the natural folding power of DNA, the building blocks of human life, to create new and useful biological structures.
As a proof-of-concept, the researchers made more than 50 nanoscale objects, including a “nano-dinosaur,” a “dancing robot” and a mini-Australia that is 150 nanometers wide, a thousand times narrower than a human hair.
Every day, researchers at the Department of Energy’s SLAC National Accelerator Laboratory tackle some of the biggest questions in science and technology—from laying the foundations for new drugs to developing new battery materials and solving big data challenges associated with particle physics and cosmology.
To get a hand with that work, they are increasingly turning to artificial intelligence. “AI will help accelerate our science and technology further,” said Ryan Coffee, a SLAC senior scientist. “I am really excited about that.”
RoVi-Aug lets robots autonomously share skills, increasing efficiency and success by 30%, all without human input.
RoVi-Aug enables robots to autonomously share skills across models, boosting efficiency and success rates by 30% without human intervention.
Researchers from the RIKEN Center for Quantum Computing and Toshiba have succeeded in building a quantum computer gate based on a double-transmon coupler (DTC), which had been proposed theoretically by Hayato Goto, Senior Fellow at Toshiba, as a device that could significantly enhance the fidelity of quantum gates. Using this, they achieved a fidelity of 99.90 percent for a two-qubit device known as a CZ gate and 99.98 percent for a single-qubit gate. This breakthrough, which was carried out as part of the Q-LEAP project, not only boosts the performance of existing noisy intermediate-scale quantum (NISQ) devices but also helps pave the way for the realization of fault-tolerant quantum computation through effective quantum error correction.
The DTC is a new kind of tunable coupler composed of two fixed-frequency transmons—a type of qubit that is relatively insensitive to charge noise—coupled through a loop with an additional Josephson junction. Its architecture addresses one of the most pressing challenges in quantum computing: the development of hardware to entangle qubits in a high-fidelity manner. High gate fidelity is essential for minimizing errors and enhancing the reliability of quantum computations. The DTC scheme stands out by achieving both suppressed residual interaction and rapid high-fidelity two-qubit gate operations, even for highly detuned qubits. Though fidelity of 99.9 percent has been routinely achieved for single-qubit gates, error rates for two-qubit gates are typically 0.5 percent or more, mainly due to interactions between the qubits known as the ZZ interaction.
The key to the current work, published in Physical Review X, is the construction of qubits using state-of-the-art fabrication techniques and gate optimization using a type of machine learning known as reinforcement learning. These approaches allowed the researchers to translate the theoretical potential of the DTC into practical application. They used these approaches to balance two types of remaining errors—leakage error and decoherence error—that remained within the system, selecting a length of 48 nanoseconds as an optimal compromise between the two error sources. Thanks to this, they achieved fidelity levels among the highest reported in the field.