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The world of computing has taken a significant leap forward with the development of the “microwave brain” chip, a low-power microchip that can compute on both ultrafast data signals and wireless communication signals. This revolutionary innovation, created by researchers at Cornell University, marks the first time a processor has harnessed the physics of microwaves to perform real-time frequency domain computation.

Detailed in the journal Nature Electronics, this groundbreaking processor is the first true microwave neural network and is fully integrated on a silicon microchip. It can handle tasks like radio signal decoding, radar target tracking, and digital data processing while consuming less than 200 milliwatts of power – an impressive feat considering its speed and efficiency.

The secret behind this technology lies in its design as a neural network, modeled after the human brain’s interconnected modes produced in tunable waveguides. This allows it to recognize patterns and learn from data, unlike traditional digital computers that rely on step-by-step instructions timed by a clock. The microwave brain processor uses analog, nonlinear behavior in the microwave regime to handle data streams at speeds of tens of gigahertz – far faster than most digital chips.

“We’ve created something that looks more like a controlled mush of frequency behaviors that can ultimately give you high-performance computation,” says Alyssa Apsel, professor of engineering and co-senior author. Bal Govind, lead author and doctoral student, explains that the chip’s programmable distortion across a wide band of frequencies allows it to be repurposed for several computing tasks.

The microwave brain processor has achieved remarkable accuracy on multiple classification tasks involving wireless signal types, comparable to digital neural networks but with a fraction of the power and size. It can perform both low-level logic functions and complex tasks like identifying bit sequences or counting binary values in high-speed data.

With its extreme sensitivity to inputs, this chip is well-suited for hardware security applications like sensing anomalies in wireless communications across multiple bands of microwave frequencies. The researchers are optimistic about the scalability of this technology and are experimenting with ways to improve its accuracy and integrate it into existing microwave and digital processing platforms.

As the world becomes increasingly dependent on data-driven technologies, innovations like the microwave brain chip have the potential to revolutionize computing and redefine what is possible in the realm of artificial intelligence and machine learning.

The article discusses how scientists have developed tiny robots that use sound waves to coordinate into large swarms, exhibiting intelligent-like behavior. This innovative technology has the potential to revolutionize various fields, including environmental remediation, healthcare, and search and rescue operations.

Led by Igor Aronson, a team of researchers created computer models to simulate the behavior of these micromachines. They found that acoustic communication allowed individual robotic agents to work together seamlessly, adapting their shape and behavior to their environment, much like a school of fish or a flock of birds.

The robots’ ability to self-organize and re-form themselves if deformed is a significant breakthrough in the field of active matter, which studies the collective behavior of self-propelled microscopic biological and synthetic agents. This new technology has the potential to tackle complex tasks such as pollution cleanup, medical treatment from inside the body, and even exploration of disaster zones.

The team’s discovery marks a significant leap toward creating smarter, more resilient, and ultimately more useful microrobots with minimal complexity. The insights from this research are crucial for designing the next generation of microrobots capable of performing complex tasks and responding to external cues in challenging environments.

While the robots in the paper were computational agents within a theoretical model, rather than physical devices that were manufactured, the simulations observed the emergence of collective intelligence that would likely appear in any experimental study with the same design. The team’s findings have opened up new possibilities for the use of sound waves as a means of controlling micro-sized robots, offering advantages over chemical signaling such as faster and farther propagation without loss of energy.

This research has far-reaching implications for various fields, including medicine, environmental science, and engineering. It highlights the potential for microrobots to be used in complex tasks such as exploration, cleanup, and medical treatment, and demonstrates their ability to self-heal and maintain collective intelligence even after breaking apart.

The development of artificial intelligence (AI) in fusion research has taken a significant leap forward. A public-private partnership between Commonwealth Fusion Systems (CFS), the U.S. Department of Energy’s Princeton Plasma Physics Laboratory (PPPL), and Oak Ridge National Laboratory has led to the creation of HEAT-ML, an AI approach that rapidly finds and simulates “magnetic shadows” in fusion vessels: safe havens protected from intense heat plasma.

HEAT-ML uses a deep neural network to learn how to calculate shadow masks, which are 3D maps of specific areas on internal components shielded from direct heat. This AI surrogate was trained using a database of approximately 1,000 SPARC simulations and can now simulate the same calculations in mere milliseconds, as opposed to the previous 30 minutes.

The goal is to create software that significantly speeds up fusion system design and enables good decision-making during operations by adjusting plasma settings to prevent potential problems. HEAT-ML was specifically designed for a small part of the SPARC tokamak under construction by CFS but has the potential to be expanded to generalize the calculation of shadow masks for exhaust systems of any shape and size, as well as other plasma-facing components.

Researchers believe that this AI breakthrough could pave the way for faster fusion system design, enabling good decision-making during operations, and potentially leading to limitless amounts of electricity on Earth.